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Found 70 papers, 12 papers with code
HW-TSC’s Participation in the WAT 2020 Indic Languages Multilingual Task
This paper describes our work in the WAT 2020 Indic Multilingual Translation Task.
HW-TSC’s Submissions to the WMT21 Biomedical Translation Task
This paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC).
HW-TSC’s Participation in the IWSLT 2022 Isometric Spoken Language Translation
This paper presents our submissions to the IWSLT 2022 Isometric Spoken Language Translation task.
HW-TSC’s Participation in the WMT 2021 News Translation Shared Task
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT 2021 News Translation Shared Task.
HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task
This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task.
HW-TSC’s Participation in the WMT 2021 Large-Scale Multilingual Translation Task
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to the WMT 2021 Large-Scale Multilingual Translation Task.
The Limits of Search Algorithms
A platform commits to a search algorithm that maps prices to search order.
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.
BP-GPT: Auditory Neural Decoding Using fMRI-prompted LLM
By using the brain representation that is extracted from the fMRI as a prompt, our method can utilize GPT-2 to decode fMRI signals into stimulus text.
UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent
Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-Language Models (VLMs) to improve the generalization capabilities.
Improving Vision-Language-Action Model with Online Reinforcement Learning
Recent studies have successfully integrated large vision-language models (VLMs) into low-level robotic control by supervised fine-tuning (SFT) with expert robotic datasets, resulting in what we term vision-language-action (VLA) models.
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
Recent advancements in robotics have focused on developing generalist policies capable of performing multiple tasks.
Diffusion-based Method for Satellite Pattern-of-Life Identification
The Expert-ML method achieved high accuracy results in simulation data and real-world data with normal sampling rate.
Prediction with Action: Visual Policy Learning via Joint Denoising Process
On the other line, diffusion models have also shown promise in robotic control tasks by denoising actions, known as diffusion policy.
FrameBridge: Improving Image-to-Video Generation with Bridge Models
By formulating I2V synthesis as a frames-to-frames generation task and modelling it with a data-to-data process, we fully exploit the information in input image and facilitate the generative model to learn the image animation process.
IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI
Through this unified latent action space, IGOR enables knowledge transfer among large-scale robot and human activity data.
HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers
Large Vision-Language-Action (VLA) models, leveraging powerful pre trained Vision-Language Models (VLMs) backends, have shown promise in robotic control due to their impressive generalization ability.
Multi-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning
Specifically, we design an augmented emotional relation graph module with label disambiguation to handle the past-missing partial label problem.
Reverse the auditory processing pathway: Coarse-to-fine audio reconstruction from fMRI
This research contributes to the comprehension of the human auditory system, pushing boundaries in neural decoding and audio reconstruction methodologies.
Open-vocabulary Auditory Neural Decoding Using fMRI-prompted LLM
By using the brain representation that is extracted from the fMRI as a prompt, our method can utilize GPT-2 to decode fMRI signals into stimulus text.
Towards Human-AI Mutual Learning: A New Research Paradigm
This paper describes a new research paradigm for studying human-AI collaboration, named "human-AI mutual learning", defined as the process where humans and AI agents preserve, exchange, and improve knowledge during human-AI collaboration.
Linearly-evolved Transformer for Pan-sharpening
In this way, our proposed method is capable of benefiting the cascaded modeling rule while achieving favorable performance in the efficient manner.
An edge detection-based deep learning approach for tear meniscus height measurement
For improved segmentation of the pupil and tear meniscus areas, the convolutional neural network Inceptionv3 was first implemented as an image quality assessment model, effectively identifying higher-quality images with an accuracy of 98. 224%.
A Novel Paradigm Boosting Translation Capabilities of Large Language Models
Experimental results conducted using the Llama2 model, particularly on Chinese-Llama2 after monolingual augmentation, demonstrate the improved translation capabilities of LLMs.
Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations
To address these challenges, this paper introduces an Efficient and Generalized end-to-end Autonomous Driving System (EGADS) for complex and varied scenarios.
R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation
Moreover, these strategies are more suitable for end-to-end systems than cascade systems.
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion
WLAC predicts a target word given a source sentence, translation context, and a human typed character sequence.
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing
It is challenging but important to save annotation efforts in streaming data acquisition to maintain data quality for supervised learning base learners.
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
$k$-Clustering in $\mathbb{R}^d$ (e. g., $k$-median and $k$-means) is a fundamental machine learning problem.
Text Style Transfer Back-Translation
To address this issue, we propose Text Style Transfer Back Translation (TST BT), which uses a style transfer model to modify the source side of BT data.
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models
With strong capabilities of reasoning and a broad understanding of the world, Large Language Models (LLMs) have demonstrated immense potential in building versatile embodied decision-making agents capable of executing a wide array of tasks.
Towards Generalizable Reinforcement Learning for Trade Execution
To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms.
TBFormer: Two-Branch Transformer for Image Forgery Localization
In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer).
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control.
Energy Efficiency Optimization of Intelligent Reflective Surface-assisted Terahertz-RSMA System
This paper examines the energy efficiency optimization problem of intelligent reflective surface (IRS)-assisted multi-user rate division multiple access (RSMA) downlink systems under terahertz propagation.
On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
Generalization in Reinforcement Learning (RL) aims to learn an agent during training that generalizes to the target environment.
Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing
In this work, we propose a Bayesian sparse regression for multivariate mixed responses to enhance the prediction of runtime performance metrics and to enable the statistical inferences.
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation.
Flow-based Recurrent Belief State Learning for POMDPs
Furthermore, we show that the learned belief states can be plugged into downstream RL algorithms to improve performance.
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning
Histology images are a natural choice for such a study.
Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports
Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning.
Distributional Reinforcement Learning for Multi-Dimensional Reward Functions
To fully inherit the benefits of distributional RL and hybrid reward architectures, we introduce Multi-Dimensional Distributional DQN (MD3QN), which extends distributional RL to model the joint return distribution from multiple reward sources.
Distributional Reinforcement Learning
reinforcement-learning
+2
Understanding Domain Randomization for Sim-to-real Transfer
Reinforcement learning encounters many challenges when applied directly in the real world.
WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition
In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of 10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about 10000 hours unlabeled speech, with 22400+ hours in total.
Ranked #6 on
Speech Recognition
on WenetSpeech
Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
In particular, we take a plug-in solver approach, where we focus on learning a model in the exploration phase and demand that \emph{any planning algorithm} on the learned model can give a near-optimal policy.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning
Histology images are a natural choice for such study.
The $f$-Divergence Reinforcement Learning Framework
In FRL, the policy evaluation and policy improvement phases are simultaneously performed by minimizing the $f$-divergence between the learning policy and sampling policy, which is distinct from conventional DRL algorithms that aim to maximize the expected cumulative rewards.
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders
Variational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications.
Adaptively Weighted Top-N Recommendation for Organ Matching
Because of the shortage, organ matching decision is the most critical decision to assign the limited viable organs to the most suitable patients.
Certifiably Robust Interpretation via Renyi Differential Privacy
The advantages of our Renyi-Robust-Smooth (RDP-based interpretation method) are three-folds.
U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition
On the experiment of AISHELL-1, we achieve a 4. 63\% character error rate (CER) with a non-streaming setup and 5. 05\% with a streaming setup with 320ms latency by U2++.
Friedel Oscillations of Vortex Bound States Under Extreme Quantum Limit in KCa2Fe4As4F2
We report the observation of discrete vortex bound states with the energy levels deviating from the widely believed ratio of 1:3:5 in the vortices of an iron based superconductor KCa2Fe4As4F2 through scanning tunneling microcopy (STM).
Superconductivity Strongly Correlated Electrons
Single particle tunneling spectroscopy and superconducting gaps in layered iron based superconductor KCa$_{2}$Fe$_{4}$As$_{4}$F$_{2}$
On the second type of surface which is rarely obtained, the fully gapped feature can still be observed on the tunneling spectra, although multiple gaps are obtained either from a single spectrum or separate ones, and the gap values determined from coherence peaks locate mainly in the range from 4 to 8 meV.
Superconductivity
Near-optimal Representation Learning for Linear Bandits and Linear RL
This paper studies representation learning for multi-task linear bandits and multi-task episodic RL with linear value function approximation.
WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit
In this paper, we propose an open source, production first, and production ready speech recognition toolkit called WeNet in which a new two-pass approach is implemented to unify streaming and non-streaming end-to-end (E2E) speech recognition in a single model.
Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events
Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation
Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.
Relation Extraction with Contextualized Relation Embedding (CRE)
This paper proposes an architecture for the relation extraction task that integrates semantic information with knowledge base modeling in a novel manner.
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL
The regret of FMDP-BF is shown to be exponentially smaller than that of optimal algorithms designed for non-factored MDPs, and improves on the best previous result for FMDPs~\citep{osband2014near} by a factored of $\sqrt{H|\mathcal{S}_i|}$, where $|\mathcal{S}_i|$ is the cardinality of the factored state subspace and $H$ is the planning horizon.
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events
Humans integrate multiple sensory modalities (e. g. visual and audio) to build a causal understanding of the physical world.
(Locally) Differentially Private Combinatorial Semi-Bandits
In this paper, we study Combinatorial Semi-Bandits (CSB) that is an extension of classic Multi-Armed Bandits (MAB) under Differential Privacy (DP) and stronger Local Differential Privacy (LDP) setting.
Non-destructive three-dimensional measurement of hand vein based on self-supervised network
At present, supervised stereo methods based on deep neural network have achieved impressive results.
High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing
In this paper, we present a convolution neural network based method to recover the light intensity distribution from the overlapped dispersive spectra instead of adding an extra light path to capture it directly for the first time.
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication
We study the problem of regret minimization for distributed bandits learning, in which $M$ agents work collaboratively to minimize their total regret under the coordination of a central server.
Passive TCP Identification for Wired and WirelessNetworks: A Long-Short Term Memory Approach
TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency.
Residual Pyramid Learning for Single-Shot Semantic Segmentation
In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the main and residuals of segmentation by decomposing the label at different levels of residual blocks.
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP
A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient.
Discrete Potts Model for Generating Superpixels on Noisy Images
Many computer vision applications, such as object recognition and segmentation, increasingly build on superpixels.
The Spaces of Data, Information, and Knowledge
We study the data space $D$ of any given data set $X$ and explain how functions and relations are defined over $D$.
Automated Generation of Geometric Theorems from Images of Diagrams
We propose an approach to generate geometric theorems from electronic images of diagrams automatically.
