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Found 28 papers, 6 papers with code
Neural Networks Models for Entity Discovery and Linking
This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests.
A Novel Way of Identifying Cyber Predators
This research proposes the combination of sentiment analysis, new approach of sentence vectors and LSTM-RNN as a novel way for Sexual Predator Identification (SPI).
The USTC-NEL Speech Translation system at IWSLT 2018
This paper describes the USTC-NEL system to the speech translation task of the IWSLT Evaluation 2018.
Learning Efficient Lexically-Constrained Neural Machine Translation with External Memory
Recent years has witnessed dramatic progress of neural machine translation (NMT), however, the method of manually guiding the translation procedure remains to be better explored.
Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i. e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection.
Enjoy the Untrusted Cloud: A Secure, Scalable and Efficient SQL-like Query Framework for Outsourcing Data
On one hand, such work lacks of supporting scalable access control over multiple data users.
Cryptography and Security Databases Distributed, Parallel, and Cluster Computing
Towards Interpretable and Robust Hand Detection via Pixel-wise Prediction
The lack of interpretability of existing CNN-based hand detection methods makes it difficult to understand the rationale behind their predictions.
Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning
However, different devices may transmit different data for different machine learning jobs and a fundamental question is how to jointly plan the UGV path, the devices' energy consumption, and the number of samples for different jobs?
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access.
The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021
This paper describes USTC-NELSLIP's submissions to the IWSLT2021 Simultaneous Speech Translation task.
Pruning Ternary Quantization
To simultaneously optimize bit-width, model size, and accuracy, we propose pruning ternary quantization (PTQ): a simple, effective, symmetric ternary quantization method.
Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset
We use these annotations to train a BERT model with multiple data augmentation strategies.
LD-ConGR: A Large RGB-D Video Dataset for Long-Distance Continuous Gesture Recognition
The dataset and experimental results presented in this paper are expected to boost the research of long-distance gesture recognition.
AdaCoach: A Virtual Coach for Training Customer Service Agents
With the development of online business, customer service agents gradually play a crucial role as an interface between the companies and their customers.
A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System
Currently, matrix decomposition is one of the most widely used collaborative filtering algorithms by using factor decomposition to effectively deal with large-scale rating matrix.
Flexible Alignment Super-Resolution Network for Multi-Contrast MRI
To tackle this problem, we propose the Flexible Alignment Super-Resolution Network (FASR-Net) for multi-contrast MRI Super-Resolution.
Teacher-Student Architecture for Knowledge Learning: A Survey
To tackle this issue, Teacher-Student architectures were first utilized in knowledge distillation, where simple student networks can achieve comparable performance to deep teacher networks.
Hyperspherical Loss-Aware Ternary Quantization
Most of the existing works use projection functions for ternary quantization in discrete space.
Hyperspherical Quantization: Toward Smaller and More Accurate Models
Model quantization enables the deployment of deep neural networks under resource-constrained devices.
Pruning On-the-Fly: A Recoverable Pruning Method without Fine-tuning
Most existing pruning works are resource-intensive, requiring retraining or fine-tuning of the pruned models for accuracy.
LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding
To break the deadlock, we present LoTE-Animal, a large-scale endangered animal dataset collected over 12 years, to foster the application of deep learning in rare species conservation.
Ternary Quantization: A Survey
Numerous research efforts have been made to compress neural network models with faster inference and higher accuracy.
Reducing the gap between streaming and non-streaming Transducer-based ASR by adaptive two-stage knowledge distillation
In this paper, we propose an adaptive two-stage knowledge distillation method consisting of hidden layer learning and output layer learning.
Teacher-Student Architecture for Knowledge Distillation: A Survey
Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement.
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
In this paper, we propose a novel multi-source domain generalization framework called EEG-DG, which leverages multiple source domains with different statistical distributions to build generalizable models on unseen target EEG data.
LiRank: Industrial Large Scale Ranking Models at LinkedIn
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
The USTC-NELSLIP Offline Speech Translation Systems for IWSLT 2022
This paper describes USTC-NELSLIP’s submissions to the IWSLT 2022 Offline Speech Translation task, including speech translation of talks from English to German, English to Chinese and English to Japanese.
Cross Attention Augmented Transducer Networks for Simultaneous Translation
This paper proposes a novel architecture, Cross Attention Augmented Transducer (CAAT), for simultaneous translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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