This page contains one-sentence summaries of cs.AI/ML/CV/CL papers announced on 2024-07-31 generated by the compressor, my personal LLM-based project.
http://arxiv.org/abs/2407.21018v1
Compressor summary: ThinK is a novel query-dependent KV cache pruning method that reduces memory costs by over 20% without compromising LLM performance on long sequences by exploiting the low-rank structure and unbalanced magnitude distribution in attention weights.
http://arxiv.org/abs/2407.21017v1
Compressor summary: The paper proposes a new image matting method using latent diffusion models and pre-trained knowledge, which achieves high resolution and detail in the mattes, and outperforms existing methods on three benchmarks.
http://arxiv.org/abs/2407.21016v1
Compressor summary: Add-SD is a diffusion model that can insert objects into realistic scenes based on text prompts, improving downstream tasks like object detection in images.
http://arxiv.org/abs/2407.21011v1
Compressor summary: CLEFT is a new method that combines large pre-trained models and context-based prompts for efficient contrastive learning of language and images in medical applications, achieving state-of-the-art results on chest X-ray and mammography datasets with reduced model and resource requirements.
http://arxiv.org/abs/2407.21009v1
Compressor summary: The text proposes a design framework that combines LLMs and human input to generate diverse and challenging math questions for training mathematical reasoning.
http://arxiv.org/abs/2407.21004v1
Compressor summary: Evolver is a system that uses large multimodal models and chain-of-evolution prompting to detect hateful memes by simulating their evolving process and extracting relevant information from similar memes.
http://arxiv.org/abs/2407.21002v1
Compressor summary: The paper introduces XHand, a method to create expressive and photo-realistic hand avatars in real-time for extended reality and gaming applications.
http://arxiv.org/abs/2407.21001v1
Compressor summary: VLMs are biased towards associating activities with the expected gender due to ingrained stereotypes and sample selection bias, leading to a 13.2% performance drop in complex scenarios.
http://arxiv.org/abs/2407.20999v1
Compressor summary: MoFO is a new fine-tuning algorithm for large language models that selects and updates parameters with the largest momentum magnitudes to prevent knowledge forgetting without accessing pre-training data or altering the original loss function.
http://arxiv.org/abs/2407.20990v1
Compressor summary: The authors propose traceable question-answering, which uses an external knowledge repository to help large language models explain their predictions in a scene understanding task, employing counterfactual reasoning and social science insights for better human explanations.
http://arxiv.org/abs/2407.20987v1
Compressor summary: The paper introduces PIXELMOD, a system that uses perceptual hashes, vector databases, and OCR to efficiently identify visually misleading images on Twitter for soft moderation.
http://arxiv.org/abs/2407.20962v1
Compressor summary: MMTrail is a large multi-modality video-language dataset with diverse topics and custom background music, designed to facilitate cross-modality studies and train advanced language models.
http://arxiv.org/abs/2407.20956v1
Compressor summary: Key points: - Continual learning (CL) faces the challenge of catastrophic forgetting due to limited memory - The paper proposes an algorithm that calibrates the gradient to guide the model in the right direction - The approach can be combined with other CL methods and is evaluated on benchmark datasets Summary: The paper presents a gradient-based method for continual learning that reduces catastrophic forgetting and improves performance by adjusting the gradient direction.
http://arxiv.org/abs/2407.20951v1
Compressor summary: This text discusses a third way to regulate AI using human rights, presenting a methodology and model for Human Rights Impact Assessment (HRIA) based on empirical analysis and case studies.
http://arxiv.org/abs/2407.20950v1
Compressor summary: The authors present dopanim, a new benchmark dataset for multi-annotator learning research, which contains challenging animal images with human-estimated likelihoods and annotator metadata.
http://arxiv.org/abs/2407.20928v1
Compressor summary: The paper introduces UniProcessor, a text-induced unified image processor for low-level vision tasks that can effectively process various degradation types and levels using multimodal control.
http://arxiv.org/abs/2407.20920v1
Compressor summary: SSPA is a new framework for multi-label image recognition that uses in-context learning, split-and-synthesize prompting, and gated dual-modal alignments to improve performance and generalizability.
http://arxiv.org/abs/2407.20918v1
Compressor summary: The paper investigates when AGM belief revision and contraction can be implemented in epistemic spaces and finds that they require precise epistemic spaces and linear change operators.
http://arxiv.org/abs/2407.20917v1
Compressor summary: The paper presents a guide to help select reinforcement learning algorithms and action-distribution families for sequential decision-making problems, with an interactive online version available.
http://arxiv.org/abs/2407.20912v1
Compressor summary: This paper proposes a new positional encoding method for directed graphs called Multi-q Magnetic Laplacian PE, which can better capture spatial relations and outperforms existing methods on various tasks.
http://arxiv.org/abs/2407.20910v1
Compressor summary: The paper proposes a new stance detection method for automated soft-moderation systems that reduces contextual false positives and improves the accuracy of warnings on social media content.
http://arxiv.org/abs/2407.20908v1
Compressor summary: DynaVol-S is a 3D generative model that learns object-centric representations from unsupervised videos by voxelizing scenes and integrating semantic features for improved novel view synthesis and scene decomposition.
http://arxiv.org/abs/2407.20906v1
Compressor summary: Key points: - The method uses Large Language Models (LLMs) to generate comprehensive reviews from scientific articles automatically. - It can process a large number of articles quickly, providing deep insights into catalysts' composition, structure, and performance. - It has a quality control strategy to ensure reliability and minimize hallucination risks. - It has expert verification and user-friendly Windows application. Summary: The authors propose an automated review generation method based on LLMs that can rapidly analyze thousands of articles on catalysts, providing valuable insights while ensuring quality and reliability.
http://arxiv.org/abs/2407.20899v1
Compressor summary: The paper proposes a natural language explanation method for image classification that uses influential neurons and activation maps to generate accurate and accessible explanations without affecting the classifier's performance.
http://arxiv.org/abs/2407.20893v1
Compressor summary: MambaCapsule is a deep neural network that improves the explainability and accuracy of ECG arrhythmia classification by using Mamba for feature extraction and Capsule networks for prediction, while mimicking human brain processing.
http://arxiv.org/abs/2407.20892v1
Compressor summary: The study analyzes the YOLOv5 object detection model, detailing its architecture, training methods, performance, and transition to PyTorch, showing its advantages for edge devices.
http://arxiv.org/abs/2407.20891v1
Compressor summary: Bayesian Low-Rank LeArning (Bella) is a framework that reduces the computational complexity of Bayesian neural networks, enabling their use in large-scale tasks and achieving comparable or better performance than conventional methods.
http://arxiv.org/abs/2407.20884v1
Compressor summary: Key points: - The paper proposes coverage criteria for testing transformer-based sentiment analysis networks - The approach uses input space partitioning and k-projection metric to generate tests with emotional features - The experiments show increased test coverage and decreased model accuracy, indicating vulnerabilities Summary: The paper presents a method to test transformer-based sentiment analysis networks for dependability using input space partitioning and k-projection metric, resulting in more covered tests and less accurate models.
http://arxiv.org/abs/2407.20879v1
Compressor summary: VariantKG is a tool that uses knowledge graphs and graph machine learning to analyze COVID-19 patient genomic data, helping understand complex genetic relationships at the RNA level.
http://arxiv.org/abs/2407.20876v1
Compressor summary: The paper presents a new method for automatically identifying ancient coins from images using computer vision techniques, which improves on existing approaches and requires less manual work.
http://arxiv.org/abs/2407.20871v1
Compressor summary: CNES is a memory-efficient technique that stores structure encoding information for evolving temporal graphs, enabling parallel vector computation and long-term/short-term neighbor structural learning.
http://arxiv.org/abs/2407.20870v1
Compressor summary: The proposed probabilistic approach improves human localization accuracy in the Metaverse era using cheap webcams and without requiring expensive hardware or strict setup constraints.
http://arxiv.org/abs/2407.20845v1
Compressor summary: The paper introduces a new framework to evaluate how well vision models understand charts by measuring channel accuracy and discriminability of image embeddings.
http://arxiv.org/abs/2407.20843v1
Compressor summary: The paper introduces DFE-IANet, a novel network that uses spectral transformation and feature interaction to detect polyps in the gastrointestinal tract with high efficiency and accuracy, achieving state-of-the-art results on the Kvasir dataset.
http://arxiv.org/abs/2407.20836v1
Compressor summary: This paper investigates the robustness of image fake detector AI models against adversarial attacks, proposing a new frequency-based post-train Bayesian attack method that can bypass them.
http://arxiv.org/abs/2407.20828v1
Compressor summary: The text discusses the capabilities, limitations, and trustworthiness of large language models (LLMs), suggesting that their intelligence should be assessed using both quantitative and qualitative measures.
http://arxiv.org/abs/2407.20824v1
Compressor summary: The DyGKT model uses a dynamic graph to track students' learning states based on their question-answering behaviors, time intervals, and evolving relationships with questions and concepts.
http://arxiv.org/abs/2407.20822v1
Compressor summary: The paper investigates the expressive power and decidability of circumscription in fragments of first-order logic, and shows that minimizing unary predicates preserves decidability while increasing complexity.
http://arxiv.org/abs/2407.20818v1
Compressor summary: The study introduces a synthetic dataset and a framework called WARM-3D that improves roadside monocular 3D detection using weak supervision from 2D labels and enhances performance across different real-world environments.
http://arxiv.org/abs/2407.20817v1
Compressor summary: The Cloud Model Improved Transformer (CMIT) method combines a leading load prediction model with a cloud model and particle swarm optimization to achieve more accurate power load forecasts in power network clusters.
http://arxiv.org/abs/2407.20806v1
Compressor summary: ARCLE is a tool to help researchers study reinforcement learning on a challenging inductive reasoning benchmark called ARC.
http://arxiv.org/abs/2407.20799v1
Compressor summary: Key points: - The paper proposes a framework for facial expression spotting using sliding windows, multi-resolution optical flow, and spatio-temporal Transformer - The method can handle subtle motions and complete micro-expressions, as well as general macro- and micro-expressions - The method uses supervised contrastive learning to enhance expression discrimination Summary: The paper presents a framework that uses sliding windows, multi-resolution optical flow, and spatio-temporal Transformer to spot facial expressions accurately, especially micro-expressions, by applying supervised contrastive learning.
http://arxiv.org/abs/2407.20798v1
Compressor summary: The text introduces a novel framework called Diffusion Augmented Agents (DAAG) that uses large language models, vision language models, and diffusion models to enhance reinforcement learning for embodied agents in simulated robotics environments.
http://arxiv.org/abs/2407.20792v1
Compressor summary: This paper investigates how students use ChatGPT for introductory programming tasks and their perceptions of the tool.
http://arxiv.org/abs/2407.20786v1
Compressor summary: Key points: - Hyperparameter optimization can cause overfitting in solubility prediction with graph-based methods - Transformer CNN, a NLP method based on smiles, performed better than graph-based methods in most cases - Using pre-set hyperparameters reduced computational effort significantly - Consistent statistical measures are important for comparison Summary: The authors compared different methods for solubility prediction and found that overfitting was an issue with hyperparameter optimization of graph-based methods. They showed that a NLP method based on smiles outperformed them in most cases, while using pre-set hyperparameters saved time and consistent statistical measures ensured fair comparison.
http://arxiv.org/abs/2407.20777v1
Compressor summary: The paper presents a new metaheuristic algorithm for the Capacitated Vehicle Routing Problem that uses feature-based guidance from a Machine Learning model and outperforms existing methods.
http://arxiv.org/abs/2407.20775v1
Compressor summary: The authors create interpretable pre-trained cardiac models for ECG and PPG data using decoder-only transformers and show their fine-tuning effectiveness for classifying atrial fibrillation.
http://arxiv.org/abs/2407.20768v1
Compressor summary: HyperMM is an end-to-end framework for multimodal learning with incomplete imaging data, which uses a conditional hypernetwork and a permutation-invariant neural network to process variable-sized inputs without imputation.
http://arxiv.org/abs/2407.20761v1
Compressor summary: The paper proposes an omniverse balanced training framework to improve the efficiency of vision-language instruct-tuning models by rebalancing the computation load across devices.
http://arxiv.org/abs/2407.20756v1
Compressor summary: SynthVLM is a novel data synthesis pipeline for Vision Large Language Models that generates and selects high-resolution images from captions, achieving SoTA performance on vision question answering tasks with reduced computational overhead and privacy concerns.
http://arxiv.org/abs/2407.20749v1
Compressor summary: The paper proposes a new tree-like search method that significantly speeds up visual re-localization without sacrificing accuracy.
http://arxiv.org/abs/2407.20743v1
Compressor summary: Meltemi 7B is an open large language model for Greek, trained on a huge corpus and adapted from Mistral, with an instruction-tuned version available.
http://arxiv.org/abs/2407.20741v1
Compressor summary: The paper proposes a method to improve the stability and performance of Physics-Informed Neural Networks by incorporating boundary and initial conditions algebraically, leading to significant reduction in fractional errors.
http://arxiv.org/abs/2407.20734v1
Compressor summary: The paper proposes a new method for multi-task learning that reduces parameters, extracts shared features, and improves performance, especially on large datasets.
http://arxiv.org/abs/2407.20730v1
Compressor summary: The paper proposes a novel method that uses language embeddings to improve point tracking in long videos by enhancing visual feature coherence and semantic consistency.
http://arxiv.org/abs/2407.20729v1
Compressor summary: The paper presents a novel safe-for-work text classifier for the Malaysian language to ensure responsible deployment of large language models.
http://arxiv.org/abs/2407.20727v1
Compressor summary: The paper introduces a text-based approach for creating high-quality 3D room scenes using generative AI and natural language prompts, making it accessible for non-experts.
http://arxiv.org/abs/2407.20708v1
Compressor summary: The authors propose SpikeYOLO, a spiking neural network-based object detection method that simplifies YOLO and uses a new spiking neuron design to improve performance and energy efficiency over previous methods.
http://arxiv.org/abs/2407.20700v1
Compressor summary: The paper introduces a method that uses a large language model and industrial knowledge to diagnose industrial problems from Return on Experience records.
http://arxiv.org/abs/2407.20695v1
Compressor summary: The research proposes a new method to identify DDoS attacks in healthcare-IoT using CNNs that analyze environmental sensor data with high accuracy.
http://arxiv.org/abs/2407.20694v1
Compressor summary: The Cross-Mapping Coherence method is a new technique that uses time-series data to discover causal connections in complex, nonlinear systems, and it outperforms existing methods in accuracy, sensitivity, and robustness.
http://arxiv.org/abs/2407.20693v1
Compressor summary: The Temporal-Spatial Perception Model (TSPM) is a framework for answering complex questions related to multimodal videos by perceiving key audio-visual cues using declarative sentence prompts and cross-modal interaction.
http://arxiv.org/abs/2407.20678v1
Compressor summary: The study evaluates example-based explainability methods for black-box machine learning models, showing their limitations when dealing with class outliers and suggesting the need for better solutions.
http://arxiv.org/abs/2407.20673v1
Compressor summary: Our proposed label-guided prompt method improves aspect category detection by representing sentences and categories using contextual and semantic information from large language models.
http://arxiv.org/abs/2407.20668v1
Compressor summary: The study presents a novel computational framework to predict opinion leaders' views and public emotions on social media using an automatic 5W1H formulation engine and enhanced LLM-based agents, achieving high fidelity and accuracy in predicting the Russia-Ukraine War sentiments.
http://arxiv.org/abs/2407.20667v1
Compressor summary: This paper explores replacing the sum operation in Kolmogorov-Arnold Networks with the average function to improve performance on machine learning tasks.
http://arxiv.org/abs/2407.20663v1
Compressor summary: The paper describes an Arabic Natural Language Understanding shared task that evaluates systems for resolving word ambiguity and identifying locations in Arabic text using two subtasks and novel datasets.
http://arxiv.org/abs/2407.20662v1
Compressor summary: The DocXPand-25k dataset provides 25,000 synthetic identity document images with diverse backgrounds and features for ID analysis research.
http://arxiv.org/abs/2407.20660v1
Compressor summary: Spatial omics and imaging AI can help understand tissue architecture by combining gene expression patterns with morphological features, either by translating them to predict gene expression or integrating them to enrich information.
http://arxiv.org/abs/2407.20657v1
Compressor summary: The paper proposes PDCL-Attack, a method that uses CLIP to generate transferable adversarial examples by guiding a generative model with text prompts based on image labels.
http://arxiv.org/abs/2407.20654v1
Compressor summary: The paper explores using smaller, domain-specific encoder language models for Italian bureaucratic and legal tasks, improving performance with prompting techniques and calibration methods.
http://arxiv.org/abs/2407.20653v1
Compressor summary: The paper proposes a feature contrastive approach in the frequency domain to generate robust adversarial examples that work across different domains and models.
http://arxiv.org/abs/2407.20651v1
Compressor summary: The paper introduces CSR, a causality-guided self-adaptive representation method for reinforcement learning agents to generalize across tasks with changing dynamics, distribution shifts, and environment variations.
http://arxiv.org/abs/2407.20650v1
Compressor summary: The paper proposes and evaluates improved line search methods that enhance the performance of stochastic gradient descent techniques for various architectures and data domains.
http://arxiv.org/abs/2407.20648v1
Compressor summary: MF2Vec is a model that uses random walks to generate multi-faceted paths, improving node embeddings and relationships in complex networks for various tasks.
http://arxiv.org/abs/2407.20647v1
Compressor summary: The paper proposes a new image re-identification method, SVLL-ReID, that uses self-supervision to improve the performance of CLIP, a large-scale vision-language pre-trained model.
http://arxiv.org/abs/2407.20643v1
Compressor summary: The Universal IHC (UIHC) analyzer is an AI model that can interpret various types of cancer and IHC images, improving objective assessment and potentially enabling personalized medicine.
http://arxiv.org/abs/2407.20642v1
Compressor summary: ClipSitu is a multimodal model that uses CLIP embeddings to predict nouns in different roles for given verbs, achieving state-of-the-art results in situation recognition and localization tasks.
http://arxiv.org/abs/2407.20640v1
Compressor summary: The paper studies pure private learning in the agnostic model, improving upper bounds for item-level and user-level privacy and presenting an efficient algorithm for learning thresholds.
http://arxiv.org/abs/2407.20633v1
Compressor summary: This paper proposes a new approach to detect driver distraction using event cameras and spiking neural networks, which offers better performance, privacy, and efficiency than existing methods.
http://arxiv.org/abs/2407.20623v1
Compressor summary: SharkTrack is an AI-enhanced software that detects, tracks, and counts sharks and rays using BRUVS footage, reducing analysis time from hours to minutes.
http://arxiv.org/abs/2407.20622v1
Compressor summary: This paper proposes a taxonomy of brain-to-language decoding methods that could potentially help people with limited articulation and advance brain-computer interface research.
http://arxiv.org/abs/2407.20611v1
Compressor summary: The paper proposes a decentralized SGD algorithm with L'evy jumps to speed up convergence in graph-based distributed learning, overcoming the entrapment problem.
http://arxiv.org/abs/2407.20601v1
Compressor summary: The text investigates how pruning and random graphs can improve the performance of Recurrent Neural Networks (RNNs) by making their architecture sparse.
http://arxiv.org/abs/2407.20600v1
Compressor summary: The paper proposes a new deep metric learning method that fuses hierarchical knowledge about image classes to improve image recognition using a triplet loss function.
http://arxiv.org/abs/2407.20597v1
Compressor summary: The authors propose two new methods to learn sheaf structures in Sheaf Neural Networks that improve performance, are intuitive, and use fewer parameters than existing methods.
http://arxiv.org/abs/2407.20592v1
Compressor summary: EgoSonics generates high-quality and synchronized audio for silent egocentric videos using latent diffusion models, enabling new applications in virtual reality and assistive technologies.
http://arxiv.org/abs/2407.20588v1
Compressor summary: The text proposes a new method using large language models to enhance agricultural machinery management, showing significant improvement over existing approaches.
http://arxiv.org/abs/2407.20584v1
Compressor summary: The Adaptive Sparse Trainer (AST) method compresses large language models by up to 16x while maintaining minimal performance loss and reducing the zero-shot accuracy gap between dense and sparse models.
http://arxiv.org/abs/2407.20582v1
Compressor summary: The paper presents a transfer learning system using image-based methods to detect segment misalignment in multimirror satellites, achieving high accuracy with binary models and intensity classification.
http://arxiv.org/abs/2407.20581v1
Compressor summary: Knesset-DictaBERT is a Hebrew language model that improves understanding of parliamentary language using the Knesset Corpus dataset.
http://arxiv.org/abs/2407.20578v1
Compressor summary: The study compares three large language models' ability to generate educational questions from slides and finds GPT-3.5 and Llama 2-Chat 13B slightly better than Flan T5 XXL in clarity and alignment.
http://arxiv.org/abs/2407.20566v1
Compressor summary: Key points: - The method learns 3D human-object spatial relation prior from 2D images in the wild - It uses a flow-based neural network to learn the 2D keypoint layout and viewports distribution - It shows improved performance on reconstruction tasks compared to previous methods Summary: The authors propose a method that learns 3D human-object spatial relation prior from 2D images using a flow-based neural network, and demonstrates its effectiveness on reconstructing human-object interactions in real-world scenarios.
http://arxiv.org/abs/2407.20564v1
Compressor summary: This paper tests large language models' ability to reason logically with general and biomedical knowledge graphs, finding they excel at general knowledge but struggle with specialized domain-knowledge and set intersections.
http://arxiv.org/abs/2407.20563v1
Compressor summary: PyramidCoder is a new framework for visual question answering that uses a single large language model to generate executable programs for complex questions by rephrasing queries, generating code, and aggregating answers in three hierarchical levels.
http://arxiv.org/abs/2407.20560v1
Compressor summary: The paper proposes a symmetry-enhanced neural network for physics-informed learning that improves accuracy, reduces parameters, and simplifies architecture compared to existing methods.
http://arxiv.org/abs/2407.20553v1
Compressor summary: The paper proposes a new framework that uses causal representation to generate accurate counterfactual outcomes for complex causal relationships in high-dimensional data.
http://arxiv.org/abs/2407.20545v1
Compressor summary: The paper proposes a simple and efficient method to encode 3D human-object spatial relations using the Human-Object Offset and a novel Stacked Normalizing Flow (StackFLOW) to infer it from monocular images for 3D human-object interaction perception.
http://arxiv.org/abs/2407.20529v1
Compressor summary: The text discusses the vulnerabilities of large language models in natural language processing, especially in terms of leaking personal data, and proposes mitigation strategies such as model editing and chroma teaming to enhance their security and resilience.
http://arxiv.org/abs/2407.20524v1
Compressor summary: The text introduces a new method, contrastive feedback mechanism (CFM), for simultaneous speech translation systems to improve quality by using unstable predictions as feedback.
http://arxiv.org/abs/2407.20516v1
Compressor summary: The text discusses the problem of undesirable knowledge in generative AI models, and surveys machine unlearning techniques that aim to address it.
http://arxiv.org/abs/2407.20515v1
Compressor summary: The paper presents a new method using chaser spacecraft image processing and CNNs to detect markers on ENVISAT for safe de-orbiting, with promising results for space sustainability.
http://arxiv.org/abs/2407.20513v1
Compressor summary: The paper introduces a method for generating graph-based knowledge representations and neural models from natural language prompts using large language models and domain expert feedback.
http://arxiv.org/abs/2407.20508v1
Compressor summary: This work proposes a spike-based graph neural network model that uses spiking dynamics and a novel feature normalization technique to improve efficiency and stability in graph representation learning on non-Euclidean data, offering competitive performance with lower computational costs than traditional GNNs.
http://arxiv.org/abs/2407.20506v1
Compressor summary: Causal exploration is a strategy that uses causal knowledge to improve the efficiency and reliability of world model learning in reinforcement learning by selecting actions that yield useful causal insights.
http://arxiv.org/abs/2407.20505v1
Compressor summary: The paper proposes a self-reflection scheme and a multi-agent debate approach to reduce hallucinations in MLLMs and interpret their causes, as well as distinguish creativity from hallucination.
http://arxiv.org/abs/2407.20502v1
Compressor summary: RDNet improves image deblurring by modeling event degradation and using a new real-world dataset, DavisMCR.
http://arxiv.org/abs/2407.20499v1
Compressor summary: The text discusses the degree-based long-tailed problem that constrains the efficacy of graph neural networks on link prediction and proposes a framework to improve the performance of tail node pairs by increasing common neighbors.
http://arxiv.org/abs/2407.20496v1
Compressor summary: Gyro-permutation is a channel rearrangement method for hierarchical N:M sparsity that improves the accuracy of compressed deep neural networks using Sparse Tensor Core technology.
http://arxiv.org/abs/2407.20485v1
Compressor summary: The paper proposes A2SF, a technique that introduces a Forgetting Factor in the Attention Score accumulation process for large language models, improving accuracy in long sequence handling.
http://arxiv.org/abs/2407.20475v1
Compressor summary: The paper proposes Distributional Mixture of Experts (DMoE), a model-independent and data-independent regression method that predicts probability distributions of targets, and evaluates its performance on molecular property prediction tasks.
http://arxiv.org/abs/2407.20471v1
Compressor summary: Relaxed Euclidean graph equivariant neural networks can learn and represent symmetry breaking in continuous groups by using relaxed weights.