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Found 91 papers, 37 papers with code
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task
This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks.
Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task
This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian.
Tencent Translation System for the WMT21 News Translation Task
Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.
Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task
This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German<->English, English<->German, Chinese<->English and English<->Chinese.
Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task
This paper describes the Tencent AI Lab’s submission of the WMT 2020 shared task on chat translation in English-German.
Training-free Diffusion Acceleration with Bottleneck Sampling
Bottleneck Sampling follows a high-low-high denoising workflow: it performs high-resolution denoising in the initial and final stages while operating at lower resolutions in intermediate steps.
The Morphology-Control Trade-Off: Insights into Soft Robotic Efficiency
Therefore, in this study, we investigate these trade-offs across tasks of differing difficulty levels using four well-used morphological complexity metrics and control complexity measured by FLOPs.
RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories
Diffusion models have achieved remarkable success across various domains.
Goku: Flow Based Video Generative Foundation Models
This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance.
Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation
Following last year, we have continued to host the WMT translation shared task this year, the second edition of the Discourse-Level Literary Translation.
Exploiting the Index Gradients for Optimization-Based Jailbreaking on Large Language Models
In this paper, we investigate the process of GCG and identify an issue of Indirect Effect, the key bottleneck of the GCG optimization.
Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capability
Our results underscore the potential of leveraging critical tokens to reduce errors in reasoning tasks, advancing the development of AI systems capable of robust logical deduction.
Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI
To strike an optimal trade-off between computational costs and segmentation performance, we propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers.
Evaluating Knowledge-based Cross-lingual Inconsistency in Large Language Models
This paper investigates the cross-lingual inconsistencies observed in Large Language Models (LLMs), such as ChatGPT, Llama, and Baichuan, which have shown exceptional performance in various Natural Language Processing (NLP) tasks.
CoAct: A Global-Local Hierarchy for Autonomous Agent Collaboration
Existing LLMs exhibit remarkable performance on various NLP tasks, but still struggle with complex real-world tasks, even equipped with advanced strategies like CoT and ReAct.
Improving Gloss-free Sign Language Translation by Reducing Representation Density
In this paper, we identify a representation density problem that could be a bottleneck in restricting the performance of gloss-free SLT.
Contrastive Learning
Gloss-free Sign Language Translation
+2
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis
Current distillation techniques often dichotomize into two distinct aspects: i) ODE Trajectory Preservation; and ii) ODE Trajectory Reformulation.
StreakNet-Arch: An Anti-scattering Network-based Architecture for Underwater Carrier LiDAR-Radar Imaging
In this paper, we introduce StreakNet-Arch, a novel signal processing architecture designed for Underwater Carrier LiDAR-Radar (UCLR) imaging systems, to address the limitations in scatter suppression and real-time imaging.
Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage
Camouflaged object detection (COD) remains a challenging task in computer vision.
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
VmambaIR: Visual State Space Model for Image Restoration
To address these challenges, we propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks.
How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments
Researchers have examined LLMs' decision-making through the lens of Game Theory.
Forward Learning of Graph Neural Networks
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
InjectTST: A Transformer Method of Injecting Global Information into Independent Channels for Long Time Series Forecasting
A channel identifier, a global mixing module and a self-contextual attention module are devised in InjectTST.
Cross-Resolution Land Cover Classification Using Outdated Products and Transformers
In this paper, we propose a Transformerbased weakly supervised method for cross-resolution land cover classification using outdated data.
Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models
Furthermore, we analyze two key factors that contribute to the cross-lingual consistency in text watermarking and propose X-SIR as a defense method against CWRA.
Unsupervised Sign Language Translation and Generation
Motivated by the success of unsupervised neural machine translation (UNMT), we introduce an unsupervised sign language translation and generation network (USLNet), which learns from abundant single-modality (text and video) data without parallel sign language data.
Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model
In this work, we investigate the potential of employing the QE model as the reward model to predict human preferences for feedback training.
DrugAssist: A Large Language Model for Molecule Optimization
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery.
Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEM
This work first discusses a significantly large design space (28 design parameters) for a finger-based soft gripper, including the rarely-explored design space of finger arrangement that is converted to various configurations to arrange individual soft fingers.
Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models
Ideally, an advanced agent should possess the ability to accurately describe a given word using an aggressive description while concurrently maximizing confusion in the conservative description, enhancing its participation in the game.
MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting
To capture the intrinsic patterns of time series, we propose a novel deep learning network architecture, named Multi-resolution Periodic Pattern Network (MPPN), for long-term series forecasting.
Cross-Drone Transformer Network for Robust Single Object Tracking
During the tracking process, a cross-drone mapping mechanism is proposed by using the surrounding information of the drone with promising tracking status as reference, assisting drones that lost targets to re-calibrate, which implements real-time cross-drone information interaction.
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution.
Cross-modality Data Augmentation for End-to-End Sign Language Translation
To tackle these challenges, we propose a novel Cross-modality Data Augmentation (XmDA) framework to transfer the powerful gloss-to-text translation capabilities to end-to-end sign language translation (i. e. video-to-text) by exploiting pseudo gloss-text pairs from the sign gloss translation model.
Ranked #4 on
Sign Language Translation
on CSL-Daily
Exploring Human-Like Translation Strategy with Large Language Models
Compared to typical machine translation that focuses solely on source-to-target mapping, LLM-based translation can potentially mimic the human translation process which might take preparatory steps to ensure high-quality translation.
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback
Therefore, we propose ParroT, a framework to enhance and regulate the translation abilities during chat based on open-source LLMs (e. g., LLaMA), human-written translation and feedback data.
Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers.
Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine
By evaluating on a number of benchmark test sets, we find that ChatGPT performs competitively with commercial translation products (e. g., Google Translate) on high-resource European languages but lags behind significantly on low-resource or distant languages.
Hyperbolic Cosine Transformer for LiDAR 3D Object Detection
The cosh-attention module reduces the space and time complexity of the attention operation.
Tencent AI Lab - Shanghai Jiao Tong University Low-Resource Translation System for the WMT22 Translation Task
Finally, our unconstrained system achieves BLEU scores of 17. 0 and 30. 4 for English to/from Livonian.
Scaling Back-Translation with Domain Text Generation for Sign Language Gloss Translation
In this paper, to overcome the limitation, we propose a Prompt based domain text Generation (PGEN) approach to produce the large-scale in-domain spoken language text data.
STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction
The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, according to the probabilities predicted by a teacher model.
Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards
In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor.
Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios
Then, Next Hybrid Strategy (NHS) is designed to stack NCB and NTB in an efficient hybrid paradigm, which boosts performance in various downstream tasks.
Ranked #294 on
Image Classification
on ImageNet
Sepsis Prediction with Temporal Convolutional Networks
We design and implement a temporal convolutional network model to predict sepsis onset.
Asynchronous Hierarchical Federated Learning
Federated Learning is a rapidly growing area of research and with various benefits and industry applications.
MoCoViT: Mobile Convolutional Vision Transformer
Recently, Transformer networks have achieved impressive results on a variety of vision tasks.
TRT-ViT: TensorRT-oriented Vision Transformer
We revisit the existing excellent Transformers from the perspective of practical application.
Network Topology Optimization via Deep Reinforcement Learning
A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.
SepViT: Separable Vision Transformer
Vision Transformers have witnessed prevailing success in a series of vision tasks.
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation
In this paper, we present a substantial step in better understanding the SOTA sequence-to-sequence (Seq2Seq) pretraining for neural machine translation~(NMT).
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation
By carefully designing experiments, we identify two representative characteristics of the data gap in source: (1) style gap (i. e., translated vs. natural text style) that leads to poor generalization capability; (2) content gap that induces the model to produce hallucination content biased towards the target language.
Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchards
It is vital for robots to recognise and localise fruits before the harvesting in natural orchards.
Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting.
Failure-averse Active Learning for Physics-constrained Systems
Active learning is a subfield of machine learning that is devised for design and modeling of systems with highly expensive sampling costs.
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation
In this work, we propose to improve the sampling procedure by selecting the most informative monolingual sentences to complement the parallel data.
TranSmart: A Practical Interactive Machine Translation System
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed.
Benchmarking Graph Neural Networks on Link Prediction
In this paper, we benchmark several existing graph neural network (GNN) models on different datasets for link predictions.
Adaptive Spatial-Temporal Inception Graph Convolutional Networks for Multi-step Spatial-Temporal Network Data Forecasting
Spatial-temporal data forecasting is of great importance for industries such as telecom network operation and transportation management.
Hierarchical Representation via Message Propagation for Robust Model Fitting
In this paper, we propose a novel hierarchical representation via message propagation (HRMP) method for robust model fitting, which simultaneously takes advantages of both the consensus analysis and the preference analysis to estimate the parameters of multiple model instances from data corrupted by outliers, for robust model fitting.
Non-Newtonian and poroelastic effects in simulations of arterial flows
In this paper, we focus on investigating the influence on hydrodynamic factors of different coupled computational models describing the interaction between an incompressible fluid and two symmetric elastic or poroelastic structures.
Fluid Dynamics Numerical Analysis Numerical Analysis
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation
In addition, experimental results demonstrate that our Multi-Task NAT is complementary to knowledge distillation, the standard knowledge transfer method for NAT.
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data.
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation
First, we train an identification model on the original training data, and use it to distinguish inactive examples and active examples by their sentence-level output probabilities.
Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network
We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market.
Cross Learning in Deep Q-Networks
In this work, we propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods, particularly in the deep Q-networks where the overestimation is exaggerated by function approximation errors.
Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network
Existing exploration strategies in reinforcement learning (RL) often either ignore the history or feedback of search, or are complicated to implement.
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search
In this way, PAD-NAS can automatically design the operations for each layer and achieve a trade-off between search space quality and model diversity.
How Does Selective Mechanism Improve Self-Attention Networks?
Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words.
Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models
In this paper, we bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table -- an interpretable table of bilingual lexicons.
Neuron Interaction Based Representation Composition for Neural Machine Translation
Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e. g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors.
Multi-Granularity Self-Attention for Neural Machine Translation
Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information.
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons
Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work.
Towards Understanding Neural Machine Translation with Word Importance
Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory.
One Model to Learn Both: Zero Pronoun Prediction and Translation
In this paper, we propose a unified and discourse-aware ZP translation approach for neural MT models.
Exploiting Sentential Context for Neural Machine Translation
In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT).
Multiple Independent Subspace Clusterings
To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.
Modeling Recurrence for Transformer
In addition to the standard recurrent neural network, we introduce a novel attentive recurrent network to leverage the strengths of both attention and recurrent networks.
Information Aggregation for Multi-Head Attention with Routing-by-Agreement
Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces.
Context-Aware Self-Attention Networks
Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies.
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement
With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation.
Learning to Refine Source Representations for Neural Machine Translation
Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process.
Exploiting Deep Representations for Neural Machine Translation
Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures.
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.
Translating Phrases in Neural Machine Translation
Otherwise, the NMT decoder generates a word from the vocabulary as the general NMT decoder does.
Neural Machine Translation Advised by Statistical Machine Translation
Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years.
Scalable Compression of Deep Neural Networks
Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems.
