Search Results for author:
Found 12 papers, 2 papers with code
ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning
Recent research on Reasoning of Large Language Models (LLMs) has sought to further enhance their performance by integrating meta-thinking -- enabling models to monitor, evaluate, and control their reasoning processes for more adaptive and effective problem-solving.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation
This work lays the groundwork for advancing LLM capabilities in complex reasoning tasks, offering a novel System2-to-System1 solution.
Epistemic Uncertainty Quantification For Pre-trained Neural Network
Specifically, we propose a gradient-based approach to assess epistemic uncertainty, analyzing the gradients of outputs relative to model parameters, and thereby indicating necessary model adjustments to accurately represent the inputs.
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks
Specifically we propose a gradient-based approach to assess epistemic uncertainty analyzing the gradients of outputs relative to model parameters and thereby indicating necessary model adjustments to accurately represent the inputs.
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transformers).
Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body Reconstruction
While 3D body reconstruction methods have made remarkable progress recently, it remains difficult to acquire the sufficiently accurate and numerous 3D supervisions required for training.
Large Sequence Models for Sequential Decision-Making: A Survey
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e. g., GPT-3 and Swin Transformer.
Gradient-based Uncertainty Attribution for Explainable Bayesian Deep Learning
Predictions made by deep learning models are prone to data perturbations, adversarial attacks, and out-of-distribution inputs.
Uncertainty-Guided Probabilistic Transformer for Complex Action Recognition
The model prediction uncertainty is used to improve both training and inference.
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms.
Towards Efficient Discrete Integration via Adaptive Quantile Queries
On a suite of UAI inference challenge benchmarks, it saves 81. 5% of WISH queries while retaining the quality of results.
DCNN-GAN: Reconstructing Realistic Image from fMRI
Visualizing the perceptual content by analyzing human functional magnetic resonance imaging (fMRI) has been an active research area.
