Search Results for author:
Found 31 papers, 15 papers with code
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
The rapid advancement of Large Language Models (LLMs) has catalyzed the development of multi-agent systems, where multiple LLM-based agents collaborate to solve complex tasks.
CAE: Repurposing the Critic as an Explorer in Deep Reinforcement Learning
CAE utilizes any linear multi-armed bandit technique and incorporates an appropriate scaling strategy, enabling efficient exploration with provable sub-linear regret bounds and practical stability.
Differentiable Information Enhanced Model-Based Reinforcement Learning
However, this presents two primary challenges: effectively utilizing differentiable information to 1) construct models with more accurate dynamic prediction and 2) enhance the stability of policy training.
MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
Knowledge editing techniques have emerged as essential tools for updating the factual knowledge of large language models (LLMs) and multimodal models (LMMs), allowing them to correct outdated or inaccurate information without retraining from scratch.
Who's the MVP? A Game-Theoretic Evaluation Benchmark for Modular Attribution in LLM Agents
Large Language Model (LLM) agents frameworks often employ modular architectures, incorporating components such as planning, reasoning, action execution, and reflection to tackle complex tasks.
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making
As agents progress, the environment adaptively generates new tasks with social structures for agents to undertake.
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games
LASE allocates a portion of its rewards to co-players as gifts, with this allocation adapting dynamically based on the social relationship -- a metric evaluating the friendliness of co-players estimated by counterfactual reasoning.
A Contextual Combinatorial Bandit Approach to Negotiation
Our approach leverages contextual combinatorial multi-armed bandits, with the bandits resolving the exploration-exploitation dilemma, and the combinatorial nature handles large action spaces.
In-Context Editing: Learning Knowledge from Self-Induced Distributions
In scenarios where language models must incorporate new information efficiently without extensive retraining, traditional fine-tuning methods are prone to overfitting, degraded generalization, and unnatural language generation.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning.
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its superior performance compared to traditional full-finetuning.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation
This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments.
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers.
E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning
Specifically, we introduce a set of learnable key-value prompts and visual prompts into self-attention and input layers, respectively, to improve the effectiveness of model fine-tuning.
Heterogeneous Value Alignment Evaluation for Large Language Models
We conduct evaluations with new auto-metric \textit{value rationality} to represent the ability of LLMs to align with specific values.
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.
Cascaded Human-Object Interaction Recognition
The interaction recognition network has two crucial parts: a relation ranking module for high-quality HOI proposal selection and a triple-stream classifier for relation prediction.
Learning Compositional Neural Information Fusion for Human Parsing
The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively.
PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points
Detecting 3D objects from a single RGB image is intrinsically ambiguous, thus requiring appropriate prior knowledge and intermediate representations as constraints to reduce the uncertainties and improve the consistencies between the 2D image plane and the 3D world coordinate.
Ranked #2 on
Monocular 3D Object Detection
on SUN RGB-D
(AP@0.15 (10 / PNet-30) metric)
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning
Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment.
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense
We propose a new 3D holistic++ scene understanding problem, which jointly tackles two tasks from a single-view image: (i) holistic scene parsing and reconstruction---3D estimations of object bounding boxes, camera pose, and room layout, and (ii) 3D human pose estimation.
3D Human Pose Estimation
Human-Object Interaction Detection
+1
Reasoning Visual Dialogs with Structural and Partial Observations
The answer to a given question is represented by a node with missing value.
Ranked #14 on
Visual Dialog
on VisDial v0.9 val
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation
Holistic 3D indoor scene understanding refers to jointly recovering the i) object bounding boxes, ii) room layout, and iii) camera pose, all in 3D.
Ranked #5 on
Monocular 3D Object Detection
on SUN RGB-D
Human-centric Indoor Scene Synthesis Using Stochastic Grammar
We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with perfect per-pixel ground truth.
Learning Human-Object Interactions by Graph Parsing Neural Networks
For a given scene, GPNN infers a parse graph that includes i) the HOI graph structure represented by an adjacency matrix, and ii) the node labels.
Ranked #32 on
Human-Object Interaction Detection
on V-COCO
Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image
We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model.
Ranked #4 on
Monocular 3D Object Detection
on SUN RGB-D
(AP@0.15 (10 / PNet-30) metric)
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
Future predictions on sequence data (e. g., videos or audios) require the algorithms to capture non-Markovian and compositional properties of high-level semantics.
Intent-aware Multi-agent Reinforcement Learning
We experiment our algorithm in a real-world problem that is non-episodic, and the number of agents and goals can vary over time.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Predicting Human Activities Using Stochastic Grammar
This paper presents a novel method to predict future human activities from partially observed RGB-D videos.
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars
We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms.
