Search Results for author:
Found 34 papers, 6 papers with code
Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging
This study gives impetus to enhance both NIR images and events, which paves the way for high-fidelity low-light imaging and neuromorphic reasoning.
AURORA:Automated Training Framework of Universal Process Reward Models via Ensemble Prompting and Reverse Verification
In this paper, we present AURORA, a novel automated framework for training universal process reward models (PRMs) using ensemble prompting and reverse verification.
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs
Inspired by the pedagogical method of "proof by counterexamples" commonly used in human mathematics education, our work aims to enhance LLMs' ability to conduct mathematical reasoning and proof through counterexamples.
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning
In our work, by imitating the human learning process, we design an Adaptive Contrastive Learning strategy.
Learning Autonomous Code Integration for Math Language Models
Recent research on tool integration for math Large Language Models (LLMs) aims to combine complementary strengths of chain-of-thought (CoT) reasoning and code execution.
SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
Recent breakthroughs in large language models (LLMs) exemplified by the impressive mathematical and scientific reasoning capabilities of the o1 model have spotlighted the critical importance of high-quality training data in advancing LLM performance across STEM disciplines.
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Usually, the model learns a universal user representation as the input for subsequent scenario-specific models.
CogniDual Framework: Self-Training Large Language Models within a Dual-System Theoretical Framework for Improving Cognitive Tasks
Cognitive psychology investigates perception, attention, memory, language, problem-solving, decision-making, and reasoning.
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought
To address these challenges, we introduce a novel learning framework, THOUGHT-LIKE-PRO In this framework, we utilize imitation learning to imitate the Chain-of-Thought (CoT) process which is verified and translated from reasoning trajectories generated by a symbolic Prolog logic engine.
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
During execution, multiple agents interact in a downstream environment and communicate intentions to enable coordinated behaviors.
Subequivariant Reinforcement Learning Framework for Coordinated Motion Control
Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases.
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks.
LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints
Integrating first-order logic constraints (FOLCs) with neural networks is a crucial but challenging problem since it involves modeling intricate correlations to satisfy the constraints.
Gram-based Attentive Neural Ordinary Differential Equations Network for Video Nystagmography Classification
Video nystagmography (VNG) is the diagnostic gold standard of benign paroxysmal positional vertigo (BPPV), which requires medical professionals to examine the direction, frequency, intensity, duration, and variation in the strength of nystagmus on a VNG video.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
Semantic maps represent the environment using a set of semantically meaningful objects.
Bayesian Deep Basis Fitting for Depth Completion with Uncertainty
By adopting a Bayesian treatment, our Bayesian Deep Basis Fitting (BDBF) approach is able to 1) predict high-quality uncertainty estimates and 2) enable depth completion with few or no sparse measurements.
Model Embedding Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL).
Variational Policy Propagation for Multi-agent Reinforcement Learning
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory
This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping.
Depth Completion via Deep Basis Fitting
In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements.
S2VG: Soft Stochastic Value Gradient method
In this paper, we propose a simple and elegant model-based reinforcement learning algorithm called soft stochastic value gradient method (S2VG).
Model-based Reinforcement Learning
reinforcement-learning
+2
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Nonlinear Distributional Gradient Temporal-Difference Learning
We devise a distributional variant of gradient temporal-difference (TD) learning.
Distributional Reinforcement Learning
Reinforcement Learning
Communication-Efficient Projection-Free Algorithm for Distributed Optimization
We demonstrate this advantage and show that the linear oracle complexity can be reduced to almost the same order of magnitude as the communication complexity, when the feasible set is polyhedral.
Projection-Free Algorithms in Statistical Estimation
Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective.
Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion
We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images.
Fast Global Convergence via Landscape of Empirical Loss
While optimizing convex objective (loss) functions has been a powerhouse for machine learning for at least two decades, non-convex loss functions have attracted fast growing interests recently, due to many desirable properties such as superior robustness and classification accuracy, compared with their convex counterparts.
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment.
Robotics
SAGA and Restricted Strong Convexity
SAGA is a fast incremental gradient method on the finite sum problem and its effectiveness has been tested on a vast of applications.
Linear convergence of SDCA in statistical estimation
In this paper, we consider stochastic dual coordinate (SDCA) {\em without} strongly convex assumption or convex assumption.
Linear Convergence of SVRG in Statistical Estimation
SVRG and its variants are among the state of art optimization algorithms for large scale machine learning problems.
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector
This paper considers the subspace clustering problem where the data contains irrelevant or corrupted features.
