Search Results for author:
Found 66 papers, 31 papers with code
Merlion: A Machine Learning Library for Time Series
We introduce Merlion, an open-source machine learning library for time series.
PandaGPT: One Model To Instruction-Follow Them All
To do so, PandaGPT combines the multimodal encoders from ImageBind and the large language models from Vicuna.
TNT: Target-driveN Trajectory Prediction
Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captured by a set of target states.
A Contrastive Framework for Neural Text Generation
Text generation is of great importance to many natural language processing applications.
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
We present WarpDrive, a flexible, lightweight, and easy-to-use open-source RL framework that implements end-to-end deep multi-agent RL on a single GPU (Graphics Processing Unit), built on PyCUDA and PyTorch.
AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System
Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease.
Language Models Can See: Plugging Visual Controls in Text Generation
MAGIC is a flexible framework and is theoretically compatible with any text generation tasks that incorporate image grounding.
Copy Is All You Need
The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary.
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
To this end, previous work either makes use of auxiliary data at parameter server to verify the received gradients (e. g., by computing validation error rate) or leverages statistic-based methods (e. g. median and Krum) to identify and remove malicious gradients from Byzantine clients.
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years.
Exploring Dense Retrieval for Dialogue Response Selection
In this study, we present a solution to directly select proper responses from a large corpus or even a nonparallel corpus that only consists of unpaired sentences, using a dense retrieval model.
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training.
CriticBench: Evaluating Large Language Models as Critic
Critique ability are crucial in the scalable oversight and self-improvement of Large Language Models (LLMs).
Momentum Decoding: Open-ended Text Generation As Graph Exploration
In this study, we formulate open-ended text generation from a new perspective, i. e., we view it as an exploration process within a directed graph.
PONE: A Novel Automatic Evaluation Metric for Open-Domain Generative Dialogue Systems
Through extensive experiments, the learning-based metrics are demonstrated that they are the most effective evaluation metrics for open-domain generative dialogue systems.
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control
In this work, we develop a novel HIL algorithm based on Adversarial Inverse Reinforcement Learning and adapt it with the Expectation-Maximization algorithm in order to directly recover a hierarchical policy from the unannotated demonstrations.
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
This work offers a hands-on reference for the research progress in deep generative models for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms.
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning
In this paper, we present a unifying framework for heterogeneous FL algorithms with {\em arbitrary} adaptive online model pruning and provide a general convergence analysis.
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots.
Cross-Lingual Phrase Retrieval
In this paper, we propose XPR, a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences.
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods.
Multi-task Learning for Low-resource Second Language Acquisition Modeling
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned.
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients
To this end, we present LSF-SAC, a novel framework that features a variational inference-based information-sharing mechanism as extra state information to assist individual agents in the value function factorization.
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery
Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Hierarchical Deep Counterfactual Regret Minimization
Imperfect Information Games (IIGs) offer robust models for scenarios where decision-makers face uncertainty or lack complete information.
A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functions
This joint distribution is used in the body of the BO acquisition functions to search for local optima during the optimization process.
Ultra-Fast, Low-Storage, Highly Effective Coarse-grained Selection in Retrieval-based Chatbot by Using Deep Semantic Hashing
Specifically, in our proposed DSHC model, a hashing optimizing module that consists of two autoencoder models is stacked on a trained dense representation model, and three loss functions are designed to optimize it.
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation
Our experimental results on the Multi-modal Brain Tumor Segmentation Challenge (BraTS) datasets outperform those of state-of-the-art segmentation baselines, with validation Dice similarity coefficients of 0. 920, 0. 897, 0. 837 for the whole tumor, tumor core, and enhancing tumor on BraTS-2020.
MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis
This encourages the multi-task design: with each DAG as a task, the MM-DAG tries to learn the multiple DAGs jointly so that their consensus and consistency are maximized.
RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection
The early detection and early diagnosis of lung cancer are crucial to improve the survival rate of lung cancer patients.
Spatio-temporal Aware Non-negative Component Representation for Action Recognition
This paper presents a novel mid-level representation for action recognition, named spatio-temporal aware non-negative component representation (STANNCR).
Action Recognition by Hierarchical Mid-level Action Elements
Realistic videos of human actions exhibit rich spatiotemporal structures at multiple levels of granularity: an action can always be decomposed into multiple finer-grained elements in both space and time.
Latent Maximum Margin Clustering
We present a maximum margin framework that clusters data using latent variables.
Beyond Actions: Discriminative Models for Contextual Group Activities
We propose a discriminative model for recognizing group activities.
Learning Class-to-Image Distance with Object Matchings
We conduct image classification by learning a class-toimage distance function that matches objects.
Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving
We present a novel graph-neural-network-based system to effectively represent large-scale 3D point clouds with the applications to autonomous driving.
Generative Dialog Policy for Task-oriented Dialog Systems
As far as we know, the existing task-oriented dialogue systems obtain the dialogue policy through classification, which can assign either a dialogue act and its corresponding parameters or multiple dialogue acts without their corresponding parameters for a dialogue action.
Twin-Finder: Integrated Reasoning Engine for Pointer-related Code Clone Detection
Detecting code clones is crucial in various software engineering tasks.
When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation
Intuitively, a dialogue model that can control the timing of talking autonomously based on the conversation context can chat with humans more naturally.
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention
We embed the attention module in a ``slow-fast'' architecture, where the slower network runs on sparsely sampled keyframes and the light-weight shallow network runs on non-keyframes at a high frame rate.
Which Kind Is Better in Open-domain Multi-turn Dialog,Hierarchical or Non-hierarchical Models? An Empirical Study
Thus, in this paper, we will measure systematically nearly all representative hierarchical and non-hierarchical models over the same experimental settings to check which kind is better.
DQSGD: DYNAMIC QUANTIZED STOCHASTIC GRADIENT DESCENT FOR COMMUNICATION-EFFICIENT DISTRIBUTED LEARNING
This paper addresses this issue by proposing a novel dynamic quantized SGD (DQSGD) framework, which enables us to optimize the quantization strategy for each gradient descent step by exploring the trade-off between communication cost and modeling error.
Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems
Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently.
DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning
Gradient quantization is an emerging technique in reducing communication costs in distributed learning.
PT-VTON: an Image-Based Virtual Try-On Network with Progressive Pose Attention Transfer
The virtual try-on system has gained great attention due to its potential to give customers a realistic, personalized product presentation in virtualized settings.
ISTIC’s Triangular Machine Translation System for WMT2021
This paper describes the ISTIC’s submission to the Triangular Machine Translation Task of Russian-to-Chinese machine translation for WMT’ 2021.
Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs
Covering skill (a. k. a., option) discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph.
Efficient Video Instance Segmentation via Tracklet Query and Proposal
In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips.
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications.
Multi-agent Deep Covering Skill Discovery
In this case, we propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition
The data preprocessing module achieves wall clutter, human motion features, and noise subspaces separation.
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning
Such an optimization problem can be relaxed and solved using the Lagrangian multiplier method to obtain the close-form optimal projection weights.
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
To this end, we propose MAC-PO, which formulates optimal prioritized experience replay for multi-agent problems as a regret minimization over the sampling weights of transitions.
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design
By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design.
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas.
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
Our key idea is to approximate the joint state space as a Kronecker graph, based on which we can directly estimate its Fiedler vector using the Laplacian spectrum of individual agents' transition graphs.
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy
In response, this paper introduces AQUILA (adaptive quantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL.
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning
This result enables us to recast multi-agent communication into a novel online clustering problem over the local observations at each agent, with messages as cluster labels and the upper bound on the return gap as clustering loss.
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning
Offline reinforcement learning aims to utilize datasets of previously gathered environment-action interaction records to learn a policy without access to the real environment.
Advantage Actor-Critic with Reasoner: Explaining the Agent's Behavior from an Exploratory Perspective
Reinforcement learning (RL) is a powerful tool for solving complex decision-making problems, but its lack of transparency and interpretability has been a major challenge in domains where decisions have significant real-world consequences.
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture
Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in high-speed communication networks are challenging due to the high computation time and resource requirements of Deep Neural Networks (DNNs), as well as lack of explainability.
Real-time Network Intrusion Detection via Decision Transformers
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e. g., network intrusion detection from a sequence of arriving packets.
Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data
Bayesian optimization (BO) has established itself as a leading strategy for efficiently optimizing expensive-to-evaluate functions.
DECIDER: A Rule-Controllable Decoding Strategy for Language Generation by Imitating Dual-System Cognitive Theory
Lexicon-based constrained decoding approaches aim to control the meaning or style of the generated text through certain target concepts.
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand.
