· ai
[Paper] Categorical Reparameterization with Denoising Diffusion models
Gradient-based optimization with categorical variables typically relies on score-function estimators, which are unbiased but noisy, or on continuous relaxations...
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- · ai
[Paper] LLM Agents for Combinatorial Efficient Frontiers: Investment Portfolio Optimization
Investment portfolio optimization is a task conducted in all major financial institutions. The Cardinality Constrained Mean-Variance Portfolio Optimization (CCP...
- · ai
[Paper] Memory Bank Compression for Continual Adaptation of Large Language Models
Large Language Models (LLMs) have become a mainstay for many everyday applications. However, as data evolve their knowledge quickly becomes outdated. Continual ...
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[Paper] A Machine Learning Framework for Off Ball Defensive Role and Performance Evaluation in Football
Evaluating off-ball defensive performance in football is challenging, as traditional metrics do not capture the nuanced coordinated movements that limit opponen...
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[Paper] The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving
State-of-the-art large language model (LLM) pipelines rely on bootstrapped reasoning loops: sampling diverse chains of thought and reinforcing the highest-scori...
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[Paper] An Agentic Framework for Neuro-Symbolic Programming
Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient. Still, it remains a time-consuming an...
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[Paper] Stochastic Actor-Critic: Mitigating Overestimation via Temporal Aleatoric Uncertainty
Off-policy actor-critic methods in reinforcement learning train a critic with temporal-difference updates and use it as a learning signal for the policy (actor)...
- · ai
[Paper] Exploring the Performance of Large Language Models on Subjective Span Identification Tasks
Identifying relevant text spans is important for several downstream tasks in NLP, as it contributes to model explainability. While most span identification appr...
- · ai
[Paper] Precision Autotuning for Linear Solvers via Contextual Bandit-Based RL
We propose a reinforcement learning (RL) framework for adaptive precision tuning of linear solvers, and can be extended to general algorithms. The framework is ...
- · ai
[Paper] Detecting Performance Degradation under Data Shift in Pathology Vision-Language Model
Vision-Language Models have demonstrated strong potential in medical image analysis and disease diagnosis. However, after deployment, their performance may dete...
- · ai
[Paper] BSAT: B-Spline Adaptive Tokenizer for Long-Term Time Series Forecasting
Long-term time series forecasting using transformers is hampered by the quadratic complexity of self-attention and the rigidity of uniform patching, which may b...
- · ai
[Paper] A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inference
Existing paradigms for inferring pedestrian crossing behavior, ranging from statistical models to supervised learning methods, demonstrate limited generalizabil...