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Found 80 papers, 35 papers with code
The NiuTrans’s Submission to the IWSLT22 English-to-Chinese Offline Speech Translation Task
This paper describes NiuTrans’s submission to the IWSLT22 English-to-Chinese (En-Zh) offline speech translation task.
The NiuTrans System for the WMT20 Quality Estimation Shared Task
This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task.
Semantic Sentence Matching via Interacting Syntax Graphs
In this paper, we formalize the task of semantic sentence matching as a problem of graph matching in which each sentence is represented as a directed graph according to its syntactic structures.
Accelerating Image Generation with Sub-path Linear Approximation Model
Diffusion models have significantly advanced the state of the art in image, audio, and video generation tasks.
Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Models
With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift.
Conformal prediction for multi-dimensional time series by ellipsoidal sets
Conformal prediction (CP) has been a popular method for uncertainty quantification because it is distribution-free, model-agnostic, and theoretically sound.
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.
CriticBench: Evaluating Large Language Models as Critic
Critique ability are crucial in the scalable oversight and self-improvement of Large Language Models (LLMs).
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
Specifically, FairSync resolves the issue by moving it to the dual space, where a central node aggregates historical fairness data into a vector and distributes it to all servers.
LLM-based Federated Recommendation
Nevertheless, applying Fed4Rec to LLM-based recommendation presents two main challenges: first, an increase in the imbalance of performance across clients, affecting the system's efficiency over time, and second, a high demand on clients' computational and storage resources for local training and inference of LLMs.
Soft Alignment of Modality Space for End-to-end Speech Translation
End-to-end Speech Translation (ST) aims to convert speech into target text within a unified model.
Post-Training Quantization for Re-parameterization via Coarse & Fine Weight Splitting
Although neural networks have made remarkable advancements in various applications, they require substantial computational and memory resources.
Do LLMs Implicitly Exhibit User Discrimination in Recommendation? An Empirical Study
Recently, Large Language Models (LLMs) have enhanced user interaction, enabling seamless information retrieval and recommendations.
Rethinking and Improving Multi-task Learning for End-to-end Speech Translation
Significant improvements in end-to-end speech translation (ST) have been achieved through the application of multi-task learning.
Flow-based Distributionally Robust Optimization
We present a computationally efficient framework, called $\texttt{FlowDRO}$, for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty sets while aiming to find continuous worst-case distribution (also called the Least Favorable Distribution, LFD) and sample from it.
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
Existing industrial anomaly detection (IAD) methods predict anomaly scores for both anomaly detection and localization.
Bridging the Gaps of Both Modality and Language: Synchronous Bilingual CTC for Speech Translation and Speech Recognition
In this study, we present synchronous bilingual Connectionist Temporal Classification (CTC), an innovative framework that leverages dual CTC to bridge the gaps of both modality and language in the speech translation (ST) task.
DPL: Decoupled Prompt Learning for Vision-Language Models
Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e. g., CLIP) to downstream tasks.
LTP-MMF: Towards Long-term Provider Max-min Fairness Under Recommendation Feedback Loops
RFL means that recommender system can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback.
Recent Advances in Direct Speech-to-text Translation
Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly.
Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation
Pre-training and fine-tuning is a paradigm for alleviating the data scarcity problem in end-to-end speech translation (E2E ST).
Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit
For the more challenging problem of pursuing multiple evading vehicles, these algorithms typically select a fixed target evading vehicle for pursuing vehicles without considering dynamic traffic situation, which significantly reduces pursuing success rate.
PolyVoice: Language Models for Speech to Speech Translation
For the speech synthesis part, we adopt the existing VALL-E X approach and build a unit-based audio language model.
CTC-based Non-autoregressive Speech Translation
Combining end-to-end speech translation (ST) and non-autoregressive (NAR) generation is promising in language and speech processing for their advantages of less error propagation and low latency.
Bridging the Granularity Gap for Acoustic Modeling
While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies and distributing the attention weights.
Computing high-dimensional optimal transport by flow neural networks
Flow-based models are widely used in generative tasks, including normalizing flow, where a neural network transports from a data distribution $P$ to a normal distribution.
Uncovering ChatGPT's Capabilities in Recommender Systems
The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond.
Generalized generalized linear models: Convex estimation and online bounds
We introduce a new computational framework for estimating parameters in generalized generalized linear models (GGLM), a class of models that extends the popular generalized linear models (GLM) to account for dependencies among observations in spatio-temporal data.
Progressive Visual Prompt Learning with Contrastive Feature Re-formation
To the best of our knowledge, we are the first to demonstrate the superior performance of visual prompts in V-L models to previous prompt-based methods in downstream tasks.
Exploring Data Geometry for Continual Learning
Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account.
Dually Enhanced Propensity Score Estimation in Sequential Recommendation
According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data.
P-MMF: Provider Max-min Fairness Re-ranking in Recommender System
In this paper, we proposed an online re-ranking model named Provider Max-min Fairness Re-ranking (P-MMF) to tackle the problem.
OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment
However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rotation annotations.
A Class-wise Non-salient Region Generalized Framework for Video Semantic Segmentation
In this paper, we propose a class-wise non-salient region generalized (CNSG) framework for the VGSS task.
Normalizing flow neural networks by JKO scheme
Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions.
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well.
Sequential Predictive Conformal Inference for Time Series
We present a new distribution-free conformal prediction algorithm for sequential data (e. g., time series), called the \textit{sequential predictive conformal inference} (\texttt{SPCI}).
Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data
We present a method for introducing a text encoder into pre-trained end-to-end speech translation systems.
Secure Decentralized IoT Service Platform Using Consortium
The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads.
Secure Decentralized IoT Service Platform
The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads.
Graded-Q Reinforcement Learning with Information-Enhanced State Encoder for Hierarchical Collaborative Multi-Vehicle Pursuit
However, existing works on MVP pay little attention to the importance of information exchange and cooperation among pursuing vehicles under the complex urban traffic environment.
Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking
This paper proposes a Hierarchical Multi-Interest Co-Network (HCN) to capture users' diverse interests in the coarse-grained ranking stage.
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The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads.
Conformal prediction set for time-series
When building either prediction intervals for regression (with real-valued response) or prediction sets for classification (with categorical responses), uncertainty quantification is essential to studying complex machine learning methods.
Invertible Neural Networks for Graph Prediction
The proposed model consists of an invertible sub-network that maps one-to-one from data to an intermediate encoded feature, which allows forward prediction by a linear classification sub-network as well as efficient generation from output labels via a parametric mixture model.
COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas
In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground.
Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos
However, effectively leveraging the audio modality in vision-specific annotated videos for action recognition is of particular challenge.
An alternative approach to train neural networks using monotone variational inequality
We propose an alternative approach to neural network training using the monotone vector field, an idea inspired by the seminal work of Juditsky and Nemirovski [Juditsky & Nemirovsky, 2019] developed originally to solve parameter estimation problems for generalized linear models (GLM) by reducing the original non-convex problem to a convex problem of solving a monotone variational inequality (VI).
A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles
This paper proposes a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework.
The NiuTrans End-to-End Speech Translation System for IWSLT 2021 Offline Task
This paper describes the submission of the NiuTrans end-to-end speech translation system for the IWSLT 2021 offline task, which translates from the English audio to German text directly without intermediate transcription.
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing Data
We develop a distribution-free, unsupervised anomaly detection method called ECAD, which wraps around any regression algorithm and sequentially detects anomalies.
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders
To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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DPR-CAE: Capsule Autoencoder with Dynamic Part Representation for Image Parsing
Parsing an image into a hierarchy of objects, parts, and relations is important and also challenging in many computer vision tasks.
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks
We not only verify the performance gain achieved, but also provide implementation-friend designs, i. e., a scalable neural network design for the agent and a virtual environment training framework.
Cooling in poor air quality environments -- Impact of fan operation on particle deposition
This study investigates particle transport behaviour arising from axial fans operating under varied aerodynamic conditions.
Fluid Dynamics Atmospheric and Oceanic Physics
Estimating Adsorption Isotherm Parameters in Chromatography via a Virtual Injection Promoting Feed-forward Neural Network
The means to obtain the adsorption isotherms is a fundamental open problem in competitive chromatography.
Solar Radiation Ramping Events Modeling Using Spatio-temporal Point Processes
Modeling and predicting solar events, particularly the solar ramping event, is critical for improving situational awareness for solar power generation systems.
HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification
Person Re-Identification (Re-ID) is of great importance to the many video surveillance systems.
PURE: An Uncertainty-aware Recommendation Framework for Maximizing Expected Posterior Utility of Platform
In this paper, we propose a novel recommendation framework which effectively utilizes the information of user uncertainty over different item dimensions and explicitly takes into consideration the impact of display policy on user in order to achieve maximal expected posterior utility for the platform.
Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery
For high-dimensional observations in real environment, e. g., Coronal Mass Ejections (CMEs) data, the learning performance of previous methods may be degraded seriously due to the complex non-Gaussian noise and the insufficiency of prior knowledge on variable structure.
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation
This eases training by highlighting easy samples that the current model has enough competence to learn.
Conformal prediction interval for dynamic time-series
We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals.
Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains
Four popular text matching methods have been exploited in the paper.
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
Extensive experiments demonstrate that MMA regularization is able to enhance the generalization ability of various modern models and achieves considerable performance improvements on CIFAR100 and TinyImageNet datasets.
SAIS: Single-stage Anchor-free Instance Segmentation
In this paper, we propose a simple yet efficientinstance segmentation approach based on the single-stage anchor-free detector, termed SAIS.
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate.
Powering Hands-on Cybersecurity Practices with Cloud Computing
In this work, we present a cloud-based platform named EZSetup for hands-on cybersecurity practice at scale and our experience of using it in class.
The NiuTrans Machine Translation Systems for WMT19
We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track.
Realistic Channel Models Pre-training
In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.
Privileged Features Distillation at Taobao Recommendations
To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available.
Deep Reinforcement Learning for Scheduling in Cellular Networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia.
Learning Representations of Categorical Feature Combinations via Self-Attention
In most current DNN based models, feature embeddings are simply concatenated for further processing by networks.
PR Product: A Substitute for Inner Product in Neural Networks
In this paper, we analyze the inner product of weight vector w and data vector x in neural networks from the perspective of vector orthogonal decomposition and prove that the direction gradient of w decreases with the angle between them close to 0 or {\pi}.
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
Accurate prediction of fading channel in future is essential to realize adaptive transmission and other methods that can save power and provide gains.
Alternating Multi-bit Quantization for Recurrent Neural Networks
Recurrent neural networks have achieved excellent performance in many applications.
A Unified Convex Surrogate for the Schatten-$p$ Norm
In this paper, we show that for any $p$, $p_1$, and $p_2 >0$ satisfying $1/p=1/p_1+1/p_2$, there is an equivalence between the Schatten-$p$ norm of one matrix and the Schatten-$p_1$ and the Schatten-$p_2$ norms of its two factor matrices.
Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods.
On the Feasibility of Distributed Kernel Regression for Big Data
Under mild conditions, we show that, with a proper number of segments, DKR leads to an estimator that is generalization consistent to the unknown regression function.
Does generalization performance of $l^q$ regularization learning depend on $q$? A negative example
To facilitate the use of $l^{q}$-regularization, we intend to seek for a modeling strategy where an elaborative selection on $q$ is avoidable.
