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Machine Learning

What image/video training data is hardest to find right now? [R]

I'm building a crowdsourced photo collection platform (contributors take photos with smartphones, we auto-label with YOLO/CLIP + enrich w...

Reddit - Machine Learning · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
Accelerating science with AI and simulations
Machine Learning

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

AI News - General · 10 min ·

All Content

[2602.14926] MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design
Machine Learning

[2602.14926] MAC-AMP: A Closed-Loop Multi-Agent Collaboration System for Multi-Objective Antimicrobial Peptide Design

The article presents MAC-AMP, a novel closed-loop multi-agent system designed for the multi-objective optimization of antimicrobial pepti...

arXiv - AI · 4 min ·
[2602.14154] A Penalty Approach for Differentiation Through Black-Box Quadratic Programming Solvers
Machine Learning

[2602.14154] A Penalty Approach for Differentiation Through Black-Box Quadratic Programming Solvers

This paper presents dXPP, a penalty-based framework for differentiating through black-box quadratic programming solvers, improving comput...

arXiv - Machine Learning · 3 min ·
[2602.14111] Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?
Machine Learning

[2602.14111] Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?

This paper evaluates the effectiveness of Sparse Autoencoders (SAEs) in recovering meaningful features from neural networks, revealing si...

arXiv - Machine Learning · 4 min ·
[2602.14890] Lifted Relational Probabilistic Inference via Implicit Learning
Machine Learning

[2602.14890] Lifted Relational Probabilistic Inference via Implicit Learning

This paper presents a novel approach to lifted relational probabilistic inference, integrating inductive learning and deductive reasoning...

arXiv - AI · 3 min ·
[2602.14108] Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures
Machine Learning

[2602.14108] Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures

This article presents a novel approach using Physics Informed PointNets (PIPN) and Geometry Aware Neural Operators (P-IGANO) to model flu...

arXiv - Machine Learning · 4 min ·
[2602.14869] Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
Llms

[2602.14869] Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution

The paper introduces Concept Influence, a method to enhance training data attribution by leveraging interpretability, improving performan...

arXiv - AI · 4 min ·
[2602.14086] Neural Optimal Transport in Hilbert Spaces: Characterizing Spurious Solutions and Gaussian Smoothing
Machine Learning

[2602.14086] Neural Optimal Transport in Hilbert Spaces: Characterizing Spurious Solutions and Gaussian Smoothing

This paper explores Neural Optimal Transport in infinite-dimensional Hilbert spaces, addressing spurious solutions and proposing a Gaussi...

arXiv - Machine Learning · 3 min ·
[2602.14795] Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs
Machine Learning

[2602.14795] Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs

This paper presents a novel resource for building complete datasets that integrate schema and ground facts for machine learning and reaso...

arXiv - Machine Learning · 4 min ·
[2602.14049] UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions
Machine Learning

[2602.14049] UniST-Pred: A Robust Unified Framework for Spatio-Temporal Traffic Forecasting in Transportation Networks Under Disruptions

The article presents UniST-Pred, a novel framework for spatio-temporal traffic forecasting that effectively addresses disruptions in tran...

arXiv - AI · 4 min ·
[2602.14721] WebWorld: A Large-Scale World Model for Web Agent Training
Machine Learning

[2602.14721] WebWorld: A Large-Scale World Model for Web Agent Training

WebWorld introduces a large-scale simulator for training web agents, utilizing over 1 million open-web interactions to enhance generaliza...

arXiv - AI · 3 min ·
[2602.14024] EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
Llms

[2602.14024] EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models

The paper introduces EIDOS, a novel approach to time series modeling that focuses on latent-space predictive learning, enhancing the stru...

arXiv - AI · 3 min ·
[2602.14017] S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services
Llms

[2602.14017] S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services

The paper presents S2SServiceBench, a multimodal benchmark designed to enhance the effectiveness of last-mile subseasonal-to-seasonal (S2...

arXiv - Machine Learning · 4 min ·
[2602.14691] Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation
Robotics

[2602.14691] Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation

This paper presents a method to eliminate planner bias in goal recognition using multi-plan dataset generation, enhancing the evaluation ...

arXiv - AI · 3 min ·
[2602.14011] KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra
Machine Learning

[2602.14011] KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra

The paper introduces KoopGen, a neural framework for modeling and predicting high-dimensional dynamical systems with continuous spectra, ...

arXiv - Machine Learning · 3 min ·
[2602.13960] Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
Machine Learning

[2602.13960] Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds

This paper explores the steady-state behavior of constant-stepsize stochastic approximation, providing explicit non-asymptotic error boun...

arXiv - Machine Learning · 4 min ·
[2602.14674] From User Preferences to Base Score Extraction Functions in Gradual Argumentation
Ai Agents

[2602.14674] From User Preferences to Base Score Extraction Functions in Gradual Argumentation

This paper introduces Base Score Extraction Functions in gradual argumentation, enhancing decision-making and AI transparency by mapping ...

arXiv - AI · 4 min ·
[2602.13958] Chemical Language Models for Natural Products: A State-Space Model Approach
Llms

[2602.13958] Chemical Language Models for Natural Products: A State-Space Model Approach

This article presents a novel approach to chemical language models specifically for natural products, showcasing the effectiveness of sta...

arXiv - AI · 3 min ·
[2602.14622] Tabular Foundation Models Can Learn Association Rules
Llms

[2602.14622] Tabular Foundation Models Can Learn Association Rules

This paper presents a model-agnostic framework for learning association rules using Tabular Foundation Models (TFMs), addressing limitati...

arXiv - Machine Learning · 3 min ·
[2602.13939] An Adaptive Model Selection Framework for Demand Forecasting under Horizon-Induced Degradation to Support Business Strategy and Operations
Machine Learning

[2602.13939] An Adaptive Model Selection Framework for Demand Forecasting under Horizon-Induced Degradation to Support Business Strategy and Operations

This article presents an adaptive model selection framework for demand forecasting, addressing challenges posed by horizon-induced degrad...

arXiv - AI · 4 min ·
[2602.14505] Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC
Machine Learning

[2602.14505] Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

This paper presents COOL-MC, a tool for verifying and explaining sepsis treatment policies using reinforcement learning, enhancing decisi...

arXiv - Machine Learning · 4 min ·
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