This page contains one-sentence summaries of cs.AI/ML/CV/CL papers announced on 2024-09-02 generated by the compressor, my personal LLM-based project.
http://arxiv.org/abs/2408.17443v1
Compressor summary: BREASE is a novel model that simulates human cognition for long-form video understanding by combining episodic memory with semantic knowledge, achieving state-of-the-art performance on multiple benchmarks.
http://arxiv.org/abs/2408.17437v1
Compressor summary: SYNTHEVEL uses large language models to create diverse test types for evaluating NLP models' performance and weaknesses.
http://arxiv.org/abs/2408.17433v1
Compressor summary: The DARES method improves robotic-assisted surgery depth estimation by adapting Depth Anything Models with Vector Low-Rank Adaptation and a reprojection loss, achieving better performance than existing techniques.
http://arxiv.org/abs/2408.17428v1
Compressor summary: This paper introduces CLOCR-C, a method that uses transformer-based language models to correct OCR errors in historical print media archives, improving downstream NLP tasks and showing the importance of socio-cultural context.
http://arxiv.org/abs/2408.17424v1
Compressor summary: CinePreGen is a visual previsualization system that uses engine-powered diffusion to enable dynamic control over camera placement and storyboarding, improving video production quality and reducing development challenges.
http://arxiv.org/abs/2408.17422v1
Compressor summary: The paper proposes a learning-free method to find actions in videos using vision-language models and iterative visual prompting.
http://arxiv.org/abs/2408.17401v1
Compressor summary: The paper investigates how users understand and trust different methods of explaining AI-based cancer risk assessments, finding that simpler text explanations are preferred over complex charts and game-theoretic approaches.
http://arxiv.org/abs/2408.17399v1
Compressor summary: The paper proposes a Knowledge Distillation strategy that uses pretrained models on real data to train smaller models on synthetic or mixed data, improving face recognition accuracy and reducing bias.
http://arxiv.org/abs/2408.17396v1
Compressor summary: The paper proposes a method to reduce bias in graphical models related to sensitive attributes by using a multi-objective optimization problem.
http://arxiv.org/abs/2408.17394v1
Compressor summary: The text proposes interpreting a neural network as an ensemble of fixed or adaptive experts, which can help prevent forgetting in continual learning.
http://arxiv.org/abs/2408.17384v1
Compressor summary: LASSO-MOGAT is a novel deep learning framework that uses gene expression, microRNA, and DNA methylation data to classify 31 types of cancer by integrating multi-omics data and protein-protein interaction networks.
http://arxiv.org/abs/2408.17383v1
Compressor summary: MoRe is a simple framework that uses the Monarch matrix class to search for optimal adapter architectures for fine-tuning pretrained models, outperforming existing techniques in terms of parameter efficiency and performance.
http://arxiv.org/abs/2408.17380v1
Compressor summary: The paper introduces a knowledge-informed model-based residual reinforcement learning framework that integrates traffic expert knowledge into a virtual environment model to enhance learning efficiency and improve CAV trajectory control in mixed traffic flow.
http://arxiv.org/abs/2408.17377v1
Compressor summary: The authors critique large language models trained on next-token prediction (NTP) due to its limitations and propose Next Distribution Prediction (NDP), which uses $n$-gram distributions instead of one-hot targets, resulting in significant improvements across various tasks.
http://arxiv.org/abs/2408.17376v1
Compressor summary: The study used data from a project to examine how environmental factors affect relapse frequency in Multiple Sclerosis patients, finding that certain variables like air pollution and weather conditions play a role.
http://arxiv.org/abs/2408.17366v1
Compressor summary: The text proposes a new method for accurate electricity demand forecasting using graph-based models that consider the spatial distribution and interconnectedness of consumers in a decentralized network structure.
http://arxiv.org/abs/2408.17362v1
Compressor summary: The paper compares three language models' performance in climate change classification tasks and evaluates their calibration of confidence scores.
http://arxiv.org/abs/2408.17356v1
Compressor summary: The research proposes using deep learning for intrusion detection in software defined networks, showing better accuracy and efficiency than traditional methods.
http://arxiv.org/abs/2408.17354v1
Compressor summary: The study presents a model-unlearning poisoning technique that increases data leakage during fine-tuning and warns against using unverified pre-trained models.
http://arxiv.org/abs/2408.17339v1
Compressor summary: The paper proposes a 4-D light field imaging framework to improve underwater image quality and depth estimation, and introduces a new dataset for this task.
http://arxiv.org/abs/2408.17337v1
Compressor summary: Key points: - The paper studies OOD detection methods for medical image analysis using two new benchmarks with artefacts - Confidence-based methods are less effective than feature-based methods, but both have limitations - A combination of both methods is suggested to improve OOD detection performance Summary: The paper compares confidence- and feature-based methods for out-of-distribution (OOD) detection in medical image analysis using new benchmarks with artefacts. It shows that both methods have weaknesses and proposes a hybrid approach.
http://arxiv.org/abs/2408.17325v1
Compressor summary: The text studies how ChatGPT's release affected condensed matter paper abstracts on arXiv, finding improved English quality for non-native speakers and changes in word usage.
http://arxiv.org/abs/2408.17324v1
Compressor summary: The paper explores how neurons within transformer models specialize for different tasks, finding task-specific clusters and suggesting an inherent structure that training refines.
http://arxiv.org/abs/2408.17322v1
Compressor summary: The authors explore different ways to interpret neuron activations in transformer models and evaluate their effectiveness using various ablation methods, finding that each method has its own advantages and disadvantages depending on the model and regime.
http://arxiv.org/abs/2408.17316v1
Compressor summary: The paper proposes using Large Language Models to incorporate domain knowledge into automated process discovery, resulting in more robust models and practical benefits, demonstrated through a case study.
http://arxiv.org/abs/2408.17313v1
Compressor summary: F-BAI is a framework that identifies the best arm under fairness constraints and shows how fairness impacts sample complexity using an instance-specific lower bound, with F-TaS as an efficient algorithm for this setting.
http://arxiv.org/abs/2408.17308v1
Compressor summary: Key points: - Machine translations have lower lexical diversity than human translations - Lexical diversity matters for literature translation - Current methods for increasing lexical diversity are rigid - The approach proposed is reranking translation candidates with a classifier - The approach achieves high lexical diversity scores for some books Summary: The paper proposes a novel method to recover lost lexical diversity in machine translations of literature by reranking translation candidates with a classifier that distinguishes between original and translated text.
http://arxiv.org/abs/2408.17286v1
Compressor summary: The paper proposes a simple and interpretable method to optimize risk-averse objectives in discounted MDPs using a stationary policy and various optimization techniques.
http://arxiv.org/abs/2408.17284v1
Compressor summary: DCUDF2 is a new method to accurately extract surfaces from complex models using self-adaptive weights and topology correction, improving on previous state-of-the-art methods.
http://arxiv.org/abs/2408.17280v1
Compressor summary: The text introduces a toolkit that helps create cost-effective Mixture-of-Domain-Experts (MOE) models or adapters, with tips and resources for using it.
http://arxiv.org/abs/2408.17274v1
Compressor summary: The paper introduces a method to reduce the size of large sparse graphs by preserving their topological structure while maintaining sparsity levels.
http://arxiv.org/abs/2408.17267v1
Compressor summary: UrBench is a comprehensive benchmark for evaluating Large Multimodal Models in complex multi-view urban scenarios, revealing their limitations and inconsistencies.
http://arxiv.org/abs/2408.17258v1
Compressor summary: The authors propose a machine learning model that predicts city-wide delivery demand by using message-passing neural networks and geospatial knowledge from large language models, achieving better performance than existing methods on real-world datasets.
http://arxiv.org/abs/2408.17255v1
Compressor summary: CDSSL is a new self-supervised learning method for predicting crystalline material properties by recovering valid structures from perturbed ones.
http://arxiv.org/abs/2408.17253v1
Compressor summary: The paper proposes a novel method to improve time series forecasting by leveraging visual models pre-trained on natural images, which outperforms existing approaches with minimal fine-tuning.
http://arxiv.org/abs/2408.17244v1
Compressor summary: This paper reviews the past 25 years of categorical data clustering methods, comparing different algorithms and their applications across various fields.
http://arxiv.org/abs/2408.17233v1
Compressor summary: The study proposes an optimization model for managing public transport disruptions using resilience as a service strategies, considering various transportation options and factors to allocate resources effectively and minimize costs and adverse effects on stakeholders.
http://arxiv.org/abs/2408.17223v1
Compressor summary: OG-Mapping uses sparse octrees and structured 3D Gaussians for efficient and robust online dense mapping, addressing redundancy, depth noise sensitivity, storage challenges, and recovery issues.
http://arxiv.org/abs/2408.17221v1
Compressor summary: The paper studies the geometry and identifiability of function spaces defined by polynomial self-attention networks using algebraic geometry tools.
http://arxiv.org/abs/2408.17197v1
Compressor summary: The paper proposes Whitening-Net, a framework to improve image classification with class imbalance by normalizing and decorrelating batch samples using ZCA whitening and two covariance-corrected modules.
http://arxiv.org/abs/2408.17190v1
Compressor summary: The paper studies a method for dealing with inconsistent information by focusing on maximal consistent subsets that allow forgetting to restore consistency, and explores its implications for non-monotonic reasoning, computational complexity, and related concepts.
http://arxiv.org/abs/2408.17182v1
Compressor summary: The paper proposes a new method, HCRAL, for object detection that improves performance by considering inconsistencies across tasks and focusing on difficult samples using a hybrid loss function and additional sample selection strategy.
http://arxiv.org/abs/2408.17181v1
Compressor summary: The study compares natural language models, finding that BERT with class imbalance mitigation performs best at extracting relevant and contextualized clinical events from electronic health records text.
http://arxiv.org/abs/2408.17180v1
Compressor summary: The paper proposes two advanced measures to quantify balance in competitive games, using win value estimations and vector quantization, and validates them in popular online games.
http://arxiv.org/abs/2408.17171v1
Compressor summary: SafeTail is a framework that uses deep learning to selectively replicate services across multiple edge servers to meet latency targets while minimizing resource usage in uncertain edge computing environments.
http://arxiv.org/abs/2408.17165v1
Compressor summary: The paper presents a polynomial time tester-learner for robustly learning non-homogeneous halfspaces with adversarial label noise, using a new method to reduce the problem to nearly homogeneous halfspaces.
http://arxiv.org/abs/2408.17163v1
Compressor summary: The paper proposes new sparse recovery algorithms based on the Optimal Brain Surgeon framework that improve accuracy-vs-sparsity in deep neural networks, with theoretical guarantees and practical performance.
http://arxiv.org/abs/2408.17162v1
Compressor summary: The paper introduces a new framework that uses deep neural networks to create feature embeddings for tabular data with numerical and categorical features, improving their representation and capture complex relationships.
http://arxiv.org/abs/2408.17154v1
Compressor summary: The study introduces a novel anomaly detection method for ECG diagnosis that significantly improves accuracy, especially for rare cardiac anomalies, and enhances clinical efficiency in emergency care settings.
http://arxiv.org/abs/2408.17150v1
Compressor summary: Our method, Multi-View Multi-Path Reasoning (MVP), reduces hallucinations in large vision-language models by enhancing their information perception and considering the certainty of answer tokens.
http://arxiv.org/abs/2408.17149v1
Compressor summary: Keypoints are important features in computer vision, but their quality scores are not easy to compare; a new framework refines and scores keypoints based on how well they survive viewpoint changes and localization accuracy.
http://arxiv.org/abs/2408.17148v1
Compressor summary: A new Boosting algorithm combines multiple classifiers with optimal sample complexity and fast runtime using a simple majority vote.
http://arxiv.org/abs/2408.17143v1
Compressor summary: This paper introduces RenDetNet, a learning-based shadow detection model that verifies shadows are real by re-rendering the scene and uses self-supervised signals for training.
http://arxiv.org/abs/2408.17139v1
Compressor summary: Flow Matching for Reaction Coordinates (FMRC) is a deep learning algorithm that efficiently identifies optimal reaction coordinates in biomolecular reversible dynamics using conditional probability and generative models.
http://arxiv.org/abs/2408.17135v1
Compressor summary: TIM is a new method for generating human-human motion sequences that uses RWKV, Causal Interactive Injection, Role-Evolving Mixing, and Localized Pattern Amulation to model temporal and interactive properties of motion while being efficient and effective.
http://arxiv.org/abs/2408.17131v1
Compressor summary: VQ4DiT is a fast post-training vector quantization method for Diffusion Transformers Models that reduces their memory usage and achieves state-of-the-art performance in image generation quality.
http://arxiv.org/abs/2408.17129v1
Compressor summary: CETExplainer is a novel post-hoc interpretability algorithm for predicting cancer drug responses that provides biologically meaningful explanations using edge-type-specific weighting and mutual information between subgraphs and predictions.
http://arxiv.org/abs/2408.17118v1
Compressor summary: The paper presents a faster method to estimate the unique global optimum of independent component analysis (ICA) using matrix representation and fewer calculations, which was previously achieved through time-consuming random initializations.