This page contains one-sentence summaries of cs.AI/ML/CV/CL papers announced on 2024-02-17 generated by the compressor, my personal LLM-based project.
http://arxiv.org/abs/2402.10211v1
Compressor summary: HiSS is a new technique for continuous sequential prediction using hierarchical state-space models that outperforms existing sequence models in real-world sensor datasets.
http://arxiv.org/abs/2402.10210v1
Compressor summary: SPIN-Diffusion is a novel technique that improves diffusion models by having them compete with previous versions in an iterative self-improvement process, outperforming conventional supervised fine-tuning and RL methods.
http://arxiv.org/abs/2402.10208v1
Compressor summary: Spectral DeTuning is a novel method that can recover the pre-fine-tuning weights of generative models, exposing a new vulnerability in large-scale models like Stable Diffusion and Mistral.
http://arxiv.org/abs/2402.10207v1
Compressor summary: RiC is a simple and adaptive method for aligning foundation models with human preferences using supervised fine-tuning and dynamic inference-time adjustment, which reduces alignment cost and complexity compared to reinforcement learning.
http://arxiv.org/abs/2402.10206v1
Compressor summary: The paper proposes a graph reduction method using an Ising model and a graph neural network that adapts to specific downstream tasks in an end-to-end fashion.
http://arxiv.org/abs/2402.10202v1
Compressor summary: The text explores connections and applications between associative memory and probabilistic modeling in artificial intelligence, such as adaptable energy functions, dynamic memory creation, capacity analysis, and clustering on the hypersphere.
http://arxiv.org/abs/2402.10200v1
Compressor summary: The study shows that large language models can reason effectively without prompting by altering the decoding process, and this approach improves performance on reasoning benchmarks.
http://arxiv.org/abs/2402.10198v1
Compressor summary: The authors study why transformers struggle with multivariate long-term forecasting and propose a lightweight model that improves performance over existing methods.
http://arxiv.org/abs/2402.10193v1
Compressor summary: The paper introduces BitDelta, a method that quantizes the additional information added during fine-tuning of large language models, enabling significant reductions in GPU memory requirements and improved multi-tenant serving and storage.
http://arxiv.org/abs/2402.10192v1
Compressor summary: The text introduces mePS, a method to explain deep learning decisions by modeling them as random walks on hypergraphs with an inductive bias inspired by quantum physics that reduces complexity.
http://arxiv.org/abs/2402.10189v1
Compressor summary: The text discusses uncertainties in Large Language Models' (LLMs) in-context learning responses, proposing a method to quantify both aleatoric and epistemic uncertainties.
http://arxiv.org/abs/2402.10186v1
Compressor summary: Machine learning can help solve electronic structure problems efficiently, but needs a method to estimate its accuracy and integrate with self-consistent field methods for better performance and interpretability.
http://arxiv.org/abs/2402.10184v1
Compressor summary: The paper proposes a tree-based information structure for reward modeling in reinforcement learning from human feedback (RLHF) to address the trilemma of diverse contexts, low labeling cost, and reliable alignment performance, and shows its superiority over chain-based methods on three NLP tasks.
http://arxiv.org/abs/2402.10178v1
Compressor summary: TDAG is a multi-agent framework that uses dynamic task decomposition and agent generation to improve the performance of LLM-based agents in complex real-world tasks by assigning subtasks to specific subagents, while ItineraryBench is a benchmark for evaluating these agents in travel planning scenarios.
http://arxiv.org/abs/2402.10177v1
Compressor summary: The paper proposes a reinforcement learning method to find disjoint clusters that efficiently allocate resources and minimize distances while respecting a threshold.
http://arxiv.org/abs/2402.10176v1
Compressor summary: The authors construct OpenMathInstruct-1, a large math instruction tuning dataset for open-source LLMs, using synthetic data from the Mixtral model and show competitive results on GSM8K and MATH benchmarks.
http://arxiv.org/abs/2402.10175v1
Compressor summary: The paper proposes a new metric to measure discourse coherence in long-form text generation and shows it performs better than existing methods.
http://arxiv.org/abs/2402.10172v1
Compressor summary: OptiMUS is an LLM-based agent that can formulate and solve linear programming problems from natural language descriptions, outperforming existing methods on easy and hard datasets.
http://arxiv.org/abs/2402.10171v1
Compressor summary: The paper investigates how much and what kind of data is needed for continual pretraining to scale language models' context lengths to 128K, finding that domain balance and length upsampling are crucial factors.
http://arxiv.org/abs/2402.10153v1
Compressor summary: The paper presents a knowledge-infused LLM-based conversational health agent for diabetic patients, which integrates external sources and analytical tools to provide accurate and personalized dietary advice.
http://arxiv.org/abs/2402.10151v1
Compressor summary: ControlLM is a method to adjust the personality traits of language models in real time, enhancing their performance on various tasks.
http://arxiv.org/abs/2402.10150v1
Compressor summary: The paper proposes a generalization of InfoNCE called f-Mutual Information in Contrastive Learning (f-MICL) using f-divergences, which can improve performance and similarity functions in self-supervised learning.
http://arxiv.org/abs/2402.10142v1
Compressor summary: Key points: - Predictor learns to predict discrete items from a stream with probabilistic multiclass prediction - Predictor has limited space and needs to output probabilities for salient items only - Stream is unbounded, non-stationary, and concept-based (from prediction games) - Two techniques are proposed: queuing of count snapshots and sparse EMA with dynamic learning rates Summary: The text proposes two efficient techniques for probabilistic multiclass prediction of salient items in a non-stationary stream of discrete concepts, motivated by prediction games.
http://arxiv.org/abs/2402.10137v1
Compressor summary: The paper introduces TOAD, a novel dataset for task-oriented dialogs with realistic app context and system response styles, and evaluates its automatic generation pipeline.
http://arxiv.org/abs/2402.10133v1
Compressor summary: The paper proposes a novel approach to personalize game content using large language models, which generates better levels than traditional methods and reduces player quitting.
http://arxiv.org/abs/2402.10130v1
Compressor summary: This paper argues that current continual learning methods are not effective for real-world applications and proposes a new benchmark to test them more accurately.
http://arxiv.org/abs/2402.10128v1
Compressor summary: GES, a new representation using Generalized Exponential Functions, improves efficiency and accuracy in 3D scene modeling compared to traditional Gaussian Splatting methods.
http://arxiv.org/abs/2402.10115v1
Compressor summary: The study aims to recreate images from EEG signals using a Transformer-encoder and a GAN network with perceptual loss for better image quality.
http://arxiv.org/abs/2402.10110v1
Compressor summary: Selective Reflection-Tuning is a method that improves instruction tuning for large language models by combining teacher and student reflection and data selection.
http://arxiv.org/abs/2402.10109v1
Compressor summary: The proposed method uses LLMs to find evidence in patient EHR data and improve diagnostic accuracy and timeliness by providing individualized risk estimates and reducing errors due to incomplete differentials.
http://arxiv.org/abs/2402.10107v1
Compressor summary: QE-CDLM is a novel diffusion language model approach that improves controllability, portability, and inference speed by quantizing task-specific embedding space and employing adaption fine-tuning.
http://arxiv.org/abs/2402.10104v1
Compressor summary: The GeoEval benchmark evaluates the ability of large and multi-modal language models to solve geometry math problems, revealing the WizardMath model as the best performer but showing the limitations of GPT-series models on rephrased problems.
http://arxiv.org/abs/2402.10099v1
Compressor summary: Any-shift prompting is a method to improve image-language model generalization by using a hierarchical architecture and probabilistic inference to connect training and test distributions in the latent space.
http://arxiv.org/abs/2402.10095v1
Compressor summary: Classification Diffusion Models combine density ratio estimation and denoising diffusion models for better learning data distributions and image generation.
http://arxiv.org/abs/2402.10093v1
Compressor summary: MIM-Refiner improves MIM models' representations by using contrastive learning on diverse intermediate layers, achieving state-of-the-art results in linear probing and low-shot classification.
http://arxiv.org/abs/2402.10083v1
Compressor summary: The study evaluates GPT-4's ability to align with human experts in assessing the quality of ophthalmology responses generated by fine-tuned large language models, finding good agreement and potential for streamlining clinical evaluation.
http://arxiv.org/abs/2402.10076v1
Compressor summary: QUICK is a new method that speeds up the computation of quantized large language models on NVIDIA GPUs by avoiding shared memory issues.
http://arxiv.org/abs/2402.10074v1
Compressor summary: GraphCBAL is a novel active learning framework for GNNs that acquires class-balanced and informative nodes for annotation to improve GNN performance in skewed class scenarios.
http://arxiv.org/abs/2402.10073v1
Compressor summary: The authors introduce a new dataset (EiBench) and method (MoEI) for enhancing emotional intelligence in large language models without sacrificing general intelligence.
http://arxiv.org/abs/2402.10066v1
Compressor summary: The paper presents NYCTALE, a neuro-inspired Transformer architecture for lung cancer diagnosis in Personalized Medicine, which accumulates evidence from CT images and makes decisions when a threshold is reached.
http://arxiv.org/abs/2402.10065v1
Compressor summary: The paper analyzes how well different methods protect data privacy in machine learning algorithms by measuring the risk of inferring whether a specific datum is present or not.
http://arxiv.org/abs/2402.10063v1
Compressor summary: BaCE addresses catastrophic forgetting in class-incremental learning by balancing causal effects from new and old data for adapting to all classes.
http://arxiv.org/abs/2402.10061v1
Compressor summary: The paper proposes a new method for fast and accurate depth estimation in Spatial Augmented Reality using event cameras and laser projectors, simplifying the process and enabling real-time interactivity.
http://arxiv.org/abs/2402.10058v1
Compressor summary: SKU is a novel unlearning framework for Large Language Models that eliminates harmful knowledge while maintaining performance on normal prompts.
http://arxiv.org/abs/2402.10052v1
Compressor summary: The text introduces deliberate imagination, a method for unlearning targeted text while maintaining LLMs' generation and understanding abilities, addressing privacy and data exposure issues.
http://arxiv.org/abs/2402.10051v1
Compressor summary: The paper introduces TOPGUN, a method that uses program synthesis to plan tool usage in black-box settings, and SwissNYF, a suite for black-box planning and verification tasks, improving the capabilities of LLMs in complex API interactions.
http://arxiv.org/abs/2402.10046v1
Compressor summary: The authors propose a new calibration metric, Logit-Smoothed ECE, which addresses some drawbacks of the existing expected calibration error (ECE) and evaluate it on pre-trained image classification models.
http://arxiv.org/abs/2402.10039v1
Compressor summary: Feature accentuation is a new method for interpreting neural network features that provides both where and what information in arbitrary input images, using a combination of parameterization, augmentation, and regularization to create naturalistic visualizations.
http://arxiv.org/abs/2402.10038v1
Compressor summary: The paper proposes a new method called RS-DPO that combines rejection sampling and direct preference optimization to fine-tune large language models using human feedback more effectively and efficiently than existing methods.
http://arxiv.org/abs/2402.10028v1
Compressor summary: The paper proposes diffusion Thompson sampling, a method that leverages diffusion models to efficiently explore correlated action spaces in contextual bandits.
http://arxiv.org/abs/2402.10024v1
Compressor summary: SAIL is a method that improves unsupervised word translation for lower-resource languages by iteratively inducing high-confidence pairs from an LLM and using them for in-context learning.
http://arxiv.org/abs/2402.10021v1
Compressor summary: The paper proposes a novel approach called SAWEC that uses wireless sensing to reduce the amount of video data transmitted and processed in mobile VR systems, improving latency and performance.
http://arxiv.org/abs/2402.10013v1
Compressor summary: Neural networks struggle to learn formal languages perfectly, but using Minimum Description Length objective helps achieve optimal generalization.
http://arxiv.org/abs/2402.10011v1
Compressor summary: The paper introduces a new method for processing geometric data using Clifford group-equivariant layers and simplicial message passing, which improves performance on various geometric tasks.
http://arxiv.org/abs/2402.10002v1
Compressor summary: MM-Point is a novel self-supervised point cloud representation learning method that leverages multi-view 2D information for better 3D object understanding and achieves state-of-the-art performance in various downstream tasks.
http://arxiv.org/abs/2402.10001v1
Compressor summary: The text describes an attack on decentralized gradient descent that can reconstruct private data of users by exploiting a vulnerability in the gossip averaging protocol.
http://arxiv.org/abs/2402.09997v1
Compressor summary: LoraRetriever is a framework that adaptively retrieves and composes multiple LoRAs for fine-tuning large language models based on input prompts.
http://arxiv.org/abs/2402.09992v1
Compressor summary: The paper introduces a novel risk-sensitive deep reinforcement learning algorithm for contextual multi-stage stochastic combinatorial optimization and shows its effectiveness in handling distribution shifts.
http://arxiv.org/abs/2402.09989v1
Compressor summary: RiVEG is a framework that uses large language models to connect multimodal tasks, improving named entity recognition and grounding in social media images.
http://arxiv.org/abs/2402.09984v1
Compressor summary: The paper introduces symmetry-breaking augmentations (SBA) to improve AI agents' ability to adapt to new teammates with unknown or diverse strategies in collaborative settings.
http://arxiv.org/abs/2402.09982v1
Compressor summary: The paper proposes a new data augmentation method for facial expression recognition using geometrical transformations and GANs, leading to high accuracy with pretrained convolutional neural networks.
http://arxiv.org/abs/2402.09977v1
Compressor summary: The text suggests a new way to make language models smaller by sharing words between different domains, which helps save space and speed up computing without hurting performance too much.
http://arxiv.org/abs/2402.09970v1
Compressor summary: ParaTAA is a novel algorithm that accelerates image generation by parallelizing and optimizing the autoregressive process of diffusion models.
http://arxiv.org/abs/2402.09967v1
Compressor summary: The text discusses the need to improve large language models' reasoning skills and explainability for more complex tasks.
http://arxiv.org/abs/2402.09966v1
Compressor summary: The text describes a new model that can generate images from text for multiple concepts, using cross-attention guidance to separate and connect the visual representation of each concept to the text prompt.
http://arxiv.org/abs/2402.09963v1
Compressor summary: The paper explains how the input-space sensitivity of transformers affects their learnability and generalization, providing a theoretical framework to understand their biases and limitations.
http://arxiv.org/abs/2402.09962v1
Compressor summary: The text discusses using deep neural networks, including a recent Vision GNN architecture called ViG, for land cover classification from satellite images, achieving state-of-the-art results.
http://arxiv.org/abs/2402.09961v1
Compressor summary: The paper proposes a model using deep Q-networks to dynamically adjust offline schedules for committed couriers in crowdsourced delivery platforms, showing benefits in platform profit and reduced lost orders.
http://arxiv.org/abs/2402.09957v1
Compressor summary: The article introduces a new algorithm to design input features for fault recognition in rotating machines using one-dimensional raw sensor data, based on histogram theory, that works with different classifiers and has been validated with three real datasets.
http://arxiv.org/abs/2402.09954v1
Compressor summary: The study investigates how large language models use in-context learning to generate persona-based human-like Chinese dialogues and identifies three key findings about prompt instructions, retrieval strategies, and corrupted demos.
http://arxiv.org/abs/2402.09949v1
Compressor summary: MWT is a tokenizer that represents common multi-word expressions as single tokens, improving efficiency and performance of large language models.
http://arxiv.org/abs/2402.09947v1
Compressor summary: The paper proposes a new method to explain probabilistic machine learning models by extending cooperative game theory and introducing distributional values, which track changes in the model output.
http://arxiv.org/abs/2402.09939v1
Compressor summary: This paper reviews generative AI's potential in addressing construction challenges and presents a framework for developing custom solutions using contract documents as an example.
http://arxiv.org/abs/2402.09934v1
Compressor summary: The paper introduces new datasets to study whataboutism, propaganda, and the tu quoque fallacy in NLP, and proposes a novel method using attention weights for negative sample mining to improve detection accuracy.
http://arxiv.org/abs/2402.09923v1
Compressor summary: CLEAN is a new Chinese MSQA dataset with diverse questions and descriptive answers that can challenge existing models.
http://arxiv.org/abs/2402.09919v1
Compressor summary: The text describes a method to infer road networks from GPS data in construction sites using a graph-based approach.
http://arxiv.org/abs/2402.09916v1
Compressor summary: BUSTER is a dataset for financial transaction entity recognition with manually and automatically annotated documents.
http://arxiv.org/abs/2402.09911v1
Compressor summary: Key points: - The paper proposes a framework to enhance LLMs using KG for open-ended question-answering - The framework combines Pseudo-Graph Generation and Atomic Knowledge Verification - The approach improves ROUGE-L and accuracy scores compared to the baseline - The framework shows generalizability across different KG sources Summary: The paper introduces a KG-based framework that enhances LLMs for open-ended questions, using Pseudo-Graph Generation and Atomic Knowledge Verification, and demonstrates its effectiveness and generalizability.
http://arxiv.org/abs/2402.09910v1
Compressor summary: DE-COP is a method to detect if copyrighted content was used in training language models by asking them multiple-choice questions based on excerpts from books and their paraphrases.
http://arxiv.org/abs/2402.09906v1
Compressor summary: GRIT is a new language model that excels at both generating and embedding text, unifying the two tasks without sacrificing performance.
http://arxiv.org/abs/2402.09900v1
Compressor summary: The text introduces memory monoids as a novel framework to improve recurrent models in reinforcement learning by proposing a better batching method.
http://arxiv.org/abs/2402.09897v1
Compressor summary: Key points: - The study aims to develop a web app for automatically classifying COVID-19 discussions on social media - The study labels and analyzes tweets using various feature extraction and machine learning methods - The study achieves high F1 scores with CNN and Linear SVC algorithms - The study provides a valuable resource for public health challenges and pandemics Summary: The study presents a web app that classifies COVID-19 tweets on social media using different machine learning techniques, reaching high accuracy with deep and traditional algorithms, and offering a useful tool for health issues during pandemics.
http://arxiv.org/abs/2402.09891v1
Compressor summary: The study finds that machine learning models trained on causal features do not generalize better across domains compared to models using all available features.
http://arxiv.org/abs/2402.09883v1
Compressor summary: Lester is a novel method that converts videos into retro-style 2D animations by segmenting and tracking objects using SAM and DeAOT, simplifying contours with Douglas-Peucker, and optionally adding facial traits, pixelation and shadows.
http://arxiv.org/abs/2402.09881v1
Compressor summary: This paper explores interpretable kernel clustering using decision trees to approximate kernel k-means, a nonlinear extension of the classic k-means algorithm, while maintaining interpretability and good approximation results.
http://arxiv.org/abs/2402.09880v1
Compressor summary: The study critically assessed 23 state-of-the-art Large Language Model benchmarks, revealing significant limitations and emphasizing the need for standardized methodologies, ethical guidelines, and a paradigm shift in evaluation.
http://arxiv.org/abs/2402.09877v1
Compressor summary: The paper proposes uniformity metrics for automated planning to create stable and predictable plans, and shows their effectiveness in various benchmarks.
http://arxiv.org/abs/2402.09872v1
Compressor summary: This paper introduces Social Reward, a framework that uses implicit feedback from social network users to assess and improve AI-generated images for text-to-image models.
http://arxiv.org/abs/2402.09865v1
Compressor summary: The paper proposes two data-driven filtering methods for object tracking that improve accuracy and reduce domain-specific design choices compared to traditional Kalman filters, especially for non-linear motion patterns.
http://arxiv.org/abs/2402.09849v1
Compressor summary: The text discusses hyperparameter selection in Gaussian processes, the challenges of evaluating GP approximations, and proposes recommendations for comparing methods based on user expectations.
http://arxiv.org/abs/2402.09844v1
Compressor summary: The paper introduces JAT, a transformer-based model that can handle various RL tasks and multimodal data types, aiming to achieve a more general, cross-domain AI model design.
http://arxiv.org/abs/2402.09841v1
Compressor summary: The paper explores how enriching text-based language models with layout information can improve their performance in document understanding tasks and compares it to multi-modal document transformers.
http://arxiv.org/abs/2402.09838v1
Compressor summary: The paper proposes a framework to model environments that change due to deployed policies in reinforcement learning and introduces a new algorithm, MDRR, that combines samples from multiple deployments for faster convergence.
http://arxiv.org/abs/2402.09836v1
Compressor summary: Key points: - MobiGeaR is a novel framework that generates mobility behaviour as a commonsense reasoning problem - It uses a context-aware chain-of-thoughts prompting technique and a mechanistic gravity model to align LLMs with mobility data - It outperforms previous methods in sample efficiency, semantic-awareness, and downstream application performance Summary: MobiGeaR is a new method that uses large language models to generate coherent and realistic mobility data by reasoning about context and intentions.
http://arxiv.org/abs/2402.09834v1
Compressor summary: GCOPE is a novel method that enhances few-shot learning in graph datasets by unifying diverse graphs during pretraining and transferring meaningful knowledge to target tasks.
http://arxiv.org/abs/2402.09830v1
Compressor summary: This paper shows how Generative Adversarial Networks can be used for fraud detection by modeling complex data distributions and preventing bot-generated transactions.
http://arxiv.org/abs/2402.09816v1
Compressor summary: The authors propose a method to improve CLIP's performance in remote sensing and medical imagery tasks by fine-tuning and cross-modal alignment of RS modality encoder.
http://arxiv.org/abs/2402.09812v1
Compressor summary: DreamMatcher is a novel method for text-to-image personalization that uses semantic matching and masking to align user-provided references with target prompts while preserving the diversity of pre-trained models.
http://arxiv.org/abs/2402.09811v1
Compressor summary: TEXTRON is a Data Programming-based approach that improves multilingual text detection using weak supervision and an ensemble of CV and DL techniques, especially for low-resource or handwritten languages like Indian scripts.
http://arxiv.org/abs/2402.09808v1
Compressor summary: Pretrained language models can capture some surface information of tokens, but struggle with others and have trouble using the knowledge effectively.
http://arxiv.org/abs/2402.09801v1
Compressor summary: The paper proposes a fine-grained unlearning framework to eliminate object hallucination in multimodal language models without paired data or expensive computation.
http://arxiv.org/abs/2402.09786v1
Compressor summary: The StyleGAN3 model generates realistic faces with a biased discriminator that affects images by gender, race, and other categories.
http://arxiv.org/abs/2402.09782v1
Compressor summary: The MC-DBN model improves stock market and heart rate forecasting by bridging semantic gaps in multi-modal data using implicit features.
http://arxiv.org/abs/2402.09781v1
Compressor summary: This paper reviews computer vision tasks in aerial data analysis, comparing hyper parameters, discussing libraries and datasets, exploring applications, and addressing challenges for future research.
http://arxiv.org/abs/2402.09780v1
Compressor summary: TinyCL is a hardware accelerator for Continuous Learning on resource-constrained autonomous systems that performs both forward and backward propagation and achieves up to 58x speedup compared to an Nvidia GPU.
http://arxiv.org/abs/2402.09773v1
Compressor summary: NutePrune is an efficient progressive pruning method that uses one intact model with multiple masks to compress Large Language Models while maintaining high performance on various tasks.
http://arxiv.org/abs/2402.09769v1
Compressor summary: SPELA is a neuroscience-inspired algorithm for Edge AI devices that uses local Hebbian learning and embedded vectors without backpropagation, achieving high performance and few-shot learning on various datasets.
http://arxiv.org/abs/2402.09765v1
Compressor summary: The paper presents a reinforcement learning method to optimize the vehicle routing problem under uncertainty and time constraints, achieving better results than an ant-colony optimization algorithm.
http://arxiv.org/abs/2402.09764v1
Compressor summary: DPRM is a framework that uses a beta distribution to adapt to human preferences and fine-tunes an LLM policy to generate preferred responses.
http://arxiv.org/abs/2402.09760v1
Compressor summary: CFIC is a new retrieval approach for RAG systems that avoids document chunking and uses hidden states to retrieve relevant evidence text for user queries efficiently and accurately.
http://arxiv.org/abs/2402.09759v1
Compressor summary: The study fine-tunes an English language model on a large Polish dataset, creating Curie-7B-v1, which generates high-quality Polish text and performs well on nine KLEJ challenges.
http://arxiv.org/abs/2402.09748v1
Compressor summary: The paper explores compression and efficient inference methods for large language models, considering their characteristics and taxonomy, and introduces some frameworks for deploying them on resource-constrained devices.
http://arxiv.org/abs/2402.09742v1
Compressor summary: AI Hospital is a framework that leverages Large Language Models for real-time interactive diagnosis and collaboration among medical agents, enhancing diagnostic accuracy.
http://arxiv.org/abs/2402.09739v1
Compressor summary: QuRating is a method for selecting high-quality pre-training data for language models based on four abstract qualities, improving their perplexity and in-context learning performance.
http://arxiv.org/abs/2402.09738v1
Compressor summary: The paper proposes a context-aware attention framework for multimodal hateful content detection in both English and non-English languages, improving performance over existing methods.
http://arxiv.org/abs/2402.09735v1
Compressor summary: The paper introduces DFORM, a framework for comparing the dynamics of different RNNs by learning a nonlinear coordinate transformation between their trajectories, which measures their orbital similarity and functional equivalence.
http://arxiv.org/abs/2402.09734v1
Compressor summary: Oblivious agents are designed to maximize human values by inferring designers' intentions and behaving according to an effective utility function that combines known and hidden sub-functions, improving alignment as their intelligence increases.
http://arxiv.org/abs/2402.09733v1
Compressor summary: The research examines if and how large language models are aware of their own hallucinations, using an experimental framework and model interpretation techniques to understand and reduce hallucination.
http://arxiv.org/abs/2402.09731v1
Compressor summary: The paper proposes a new CNN-based method for video matting that separates the target body and edge estimation and improves edge accuracy with a novel loss function.
http://arxiv.org/abs/2402.09730v1
Compressor summary: The paper proposes DOF, an efficient framework for calculating second-order differential operators in PDEs using deep learning, which improves efficiency and memory consumption over existing methods.
http://arxiv.org/abs/2402.09727v1
Compressor summary: ReadAgent is a system that uses LLMs to read long documents interactively, store relevant content in gist memories, and improve performance on reading comprehension tasks.
http://arxiv.org/abs/2402.09725v1
Compressor summary: The paper proposes EECR, a training approach for CMLM to address data distribution discrepancy between training and inference in mask-predict frameworks for natural language processing tasks.
http://arxiv.org/abs/2402.09724v1
Compressor summary: The paper introduces a new region feature descriptor that simulates affine transformations using classification, improving feature matching accuracy under high affine transformations.
http://arxiv.org/abs/2402.09717v1
Compressor summary: The study introduces a new concept of "I Know" hallucination in image question-answering and proposes a benchmark, instructions database, and methods to reduce it.
http://arxiv.org/abs/2402.09712v1
Compressor summary: This paper shows how diffusion models with cross-attention can help learn disentangled representations from images without complex designs or additional regularization.
http://arxiv.org/abs/2402.09711v1
Compressor summary: This paper introduces NodeDup, an augmentation technique for graph neural networks that improves their link prediction performance on low-degree nodes by duplicating them and creating links with the duplicates.
http://arxiv.org/abs/2402.09702v1
Compressor summary: The Sparse Explanation Value (SEV) measures how few features are needed to explain a decision in machine learning models, and the paper proposes algorithms to reduce SEV for more interpretable explanations.
http://arxiv.org/abs/2402.09696v1
Compressor summary: The authors create a new dataset for Esperanto grammar error correction and show that GPT-4 performs better than GPT-3.5 in detecting errors in this low-resource language.
http://arxiv.org/abs/2402.09695v1
Compressor summary: The paper proposes a black-box reward poisoning attack on general offline reinforcement learning with deep neural networks, which makes low-performing policies look high-performing and vice versa.
http://arxiv.org/abs/2402.09694v1
Compressor summary: The paper proposes a low-light image enhancement method that uses pre-trained generators and a novel optimization strategy to produce high-quality images without training on low-light datasets.
http://arxiv.org/abs/2402.09676v1
Compressor summary: HyperMagNet is a hypergraph neural network that uses a non-reversible Markov chain to represent hypergraphs and a complex Hermitian Laplacian matrix for node classification tasks.
http://arxiv.org/abs/2402.09674v1
Compressor summary: The paper introduces PAL, a black-box attack on large language models that achieves high success rates, and other related techniques for testing and improving LLM safety.
http://arxiv.org/abs/2402.09671v1
Compressor summary: The text describes a new attack method that uses the alpha layer of PNG images to fool AI vision systems across various domains, requiring retraining and architectural changes for mitigation.
http://arxiv.org/abs/2402.09668v1
Compressor summary: The paper explores efficient ways to pre-train large language models by using techniques that balance model quality and resource/data usage, such as Ask-LLM (quality assessment) and Density (diverse sampling).
http://arxiv.org/abs/2402.09666v1
Compressor summary: The paper proposes a method called EntailE to improve commonsense knowledge graph construction by using textual entailment to find implicit relations between nodes with similar plausibility, densifying the graph and enriching node representations.
http://arxiv.org/abs/2402.09663v1
Compressor summary: The paper proposes a simple and effective method to classify hand shapes using multiscale template matching and background subtraction.
http://arxiv.org/abs/2402.09660v1
Compressor summary: This paper surveys user modeling and profiling techniques in AI systems, highlighting their evolution, current trends, challenges, and applications in various domains.
http://arxiv.org/abs/2402.09656v1
Compressor summary: This paper studies how editing Large Language Models (LLMs) can cause performance degradation or even model collapse, and proposes using perplexity as a faster alternative to benchmarking LLMs after each edit. It also introduces a new dataset for future research on this topic.
http://arxiv.org/abs/2402.09654v1
Compressor summary: The study explores how feedback affects GPT-4's confidence in answering USMLE questions for healthcare applications.
http://arxiv.org/abs/2402.09650v1
Compressor summary: Key points: - Computer vision advances can track and estimate poses of sports players, but predicting soccer fouls is challenging - Research introduces deep learning approach to anticipate soccer fouls using video data, bounding box positions, image details, and pose information - Model combines CNNs and RNNs to merge information from four modalities - Experimental results show that all components of the model are useful for foul prediction Summary: The research presents a novel deep learning method that uses video data and pose information to predict soccer fouls, integrating CNNs and RNNs to combine multiple modalities effectively.
http://arxiv.org/abs/2402.09642v1
Compressor summary: The paper presents InBedder, a text embedder that uses abstractive question answering to capture user-specified characteristics of texts and follows instructions better than previous approaches, with high interpretability.
http://arxiv.org/abs/2402.09638v1
Compressor summary: This survey explores multi-fidelity optimization (MFO), a cost-effective strategy for black-box optimization using hierarchical fidelity models, across various domains and highlights its challenges and prospects.
http://arxiv.org/abs/2402.09635v1
Compressor summary: The paper presents a deep learning method for aligning UAV images without using Lucas-Kanade based techniques, achieving superior performance by predicting image corners or homography matrices with a two-branch CNN.
http://arxiv.org/abs/2402.09631v1
Compressor summary: The paper proposes new methods for improving language models by making their representations less biased and less toxic, using techniques based on the Earth Mover's problem and Gaussian assumptions.