Search Results for author:
Found 45 papers, 15 papers with code
AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment.
Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents
To address this challenge, we propose MIRROR, a chain-of-thought approach that generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations.
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.
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.
An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis
Cross-attention combines image and text data for better feature extraction, and ordered regression loss ensures stable learning.
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
To address the gap, we introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
LMGT: Optimizing Exploration-Exploitation Balance in Reinforcement Learning through Language Model Guided Trade-offs
The uncertainty inherent in the environmental transition model of Reinforcement Learning (RL) necessitates a careful balance between exploration and exploitation to optimize the use of computational resources for accurately estimating an agent's expected reward.
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.
Promoting Equality in Large Language Models: Identifying and Mitigating the Implicit Bias based on Bayesian Theory
Large language models (LLMs) are trained on extensive text corpora, which inevitably include biased information.
Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debate
MLLMs often generate outputs that are inconsistent with the visual content, a challenge known as hallucination.
Robust Deep Hawkes Process under Label Noise of Both Event and Occurrence
To the best of our knowledge, this is the first study to successfully address both event and time label noise in deep Hawkes process models, offering a promising solution for medical applications, specifically in diagnosing OSAHS.
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.
Struct-X: Enhancing Large Language Models Reasoning with Structured Data
Structured data, rich in logical and relational information, has the potential to enhance the reasoning abilities of large language models (LLMs).
MINDECHO: Role-Playing Language Agents for Key Opinion Leaders
Large language models~(LLMs) have demonstrated impressive performance in various applications, among which role-playing language agents (RPLAs) have engaged a broad user base.
AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications
We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications.
Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance
In this study, we introduce a methodology for human image animation by leveraging a 3D human parametric model within a latent diffusion framework to enhance shape alignment and motion guidance in curernt human generative techniques.
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.
InfMLLM: A Unified Framework for Visual-Language Tasks
Our experiments demonstrate that preserving the positional information of visual embeddings through the pool-adapter is particularly beneficial for tasks like visual grounding.
Ranked #174 on
Visual Question Answering
on MM-Vet
FuXi: A cascade machine learning forecasting system for 15-day global weather forecast
Over the past few years, due to the rapid development of machine learning (ML) models for weather forecasting, state-of-the-art ML models have shown superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a spatial resolution of 0. 25 degree.
Communication Efficient SGD via Gradient Sampling With Bayes Prior
Different from all of them, we regard large and small gradients selection as the exploitation and exploration of gradient information, respectively.
Structure First Detail Next: Image Inpainting with Pyramid Generator
Recent deep generative models have achieved promising performance in image inpainting.
Accelerating Gossip SGD with Periodic Global Averaging
Communication overhead hinders the scalability of large-scale distributed training.
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training
Experimental results on a variety of computer vision tasks and models demonstrate that DecentLaM promises both efficient and high-quality training.
Learning Position and Target Consistency for Memory-based Video Object Segmentation
To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM.
Multiple Object Tracking with Correlation Learning
Recent works have shown that convolutional networks have substantially improved the performance of multiple object tracking by simultaneously learning detection and appearance features.
Few-Shot Incremental Learning with Continually Evolved Classifiers
First, we adopt a simple but effective decoupled learning strategy of representations and classifiers that only the classifiers are updated in each incremental session, which avoids knowledge forgetting in the representations.
Ranked #9 on
Few-Shot Class-Incremental Learning
on CIFAR-100
class-incremental learning
Few-Shot Class-Incremental Learning
+1
Self-supervised Video Representation Learning by Context and Motion Decoupling
A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias.
Practical Relative Order Attack in Deep Ranking
In this paper, we formulate a new adversarial attack against deep ranking systems, i. e., the Order Attack, which covertly alters the relative order among a selected set of candidates according to an attacker-specified permutation, with limited interference to other unrelated candidates.
Large-Scale Training System for 100-Million Classification at Alibaba
In the last decades, extreme classification has become an essential topic for deep learning.
Fashion Focus: Multi-modal Retrieval System for Video Commodity Localization in E-commerce
Nowadays, live-stream and short video shopping in E-commerce have grown exponentially.
Large-Scale Visual Search with Binary Distributed Graph at Alibaba
For a deployed visual search system with several billions of online images in total, building a billion-scale offline graph in hours is essential, which is almost unachievable by most existing methods.
Distribution Adaptive INT8 Quantization for Training CNNs
Researches have demonstrated that low bit-width (e. g., INT8) quantization can be employed to accelerate the inference process.
Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning
However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy.
Large Scale Long-tailed Product Recognition System at Alibaba
In this paper, we present a novel side information based large scale visual recognition co-training~(SICoT) system to deal with the long tail problem by leveraging the image related side information.
Virtual ID Discovery from E-commerce Media at Alibaba: Exploiting Richness of User Click Behavior for Visual Search Relevance
Benefiting from exploration of user click data, our networks are more effective to encode richer supervision and better distinguish real-shot images in terms of category and feature.
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Adapting semantic segmentation models to new domains is an important but challenging problem.
Weakly Supervised Learning with Side Information for Noisy Labeled Images
In many real-world datasets, like WebVision, the performance of DNN based classifier is often limited by the noisy labeled data.
Balanced Order Batching with Task-Oriented Graph Clustering
In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem.
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction
In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients.
Can Sophisticated Dispatching Strategy Acquired by Reinforcement Learning? - A Case Study in Dynamic Courier Dispatching System
In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-service platform of Alibaba.
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce.
Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application
For better utilizing the correlation between different ranking steps, in this paper, we propose to use reinforcement learning (RL) to learn an optimal ranking policy which maximizes the expected accumulative rewards in a search session.
Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method
The objective is to find a way to place these items that can minimize the surface area of the bin.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
