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Found 24 papers, 5 papers with code
Neural Program Synthesis with Query
In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers
Our experimental results show that the certified accuracy is increased from 36. 3% (the state-of-the-art certified detection) to 60. 4% on the ImageNet dataset, largely pushing the certified defenses for practical use.
Learning Controllable Elements Oriented Representations for Reinforcement Learning
Deep Reinforcement Learning (deep RL) has been successfully applied to solve various decision-making problems in recent years.
Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
In this framework, the environment can be easily configured to realize all kinds of RL tasks in the mainstream research.
Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment
In this paper, we propose to replace the state value function with a novel hindsight value function, which leverages the information from the future to reduce the variance of the gradient estimate for stochastic dynamic environments.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (Zero-Shot)
on CMRC 2018
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+19
Improving Dialogue Breakdown Detection with Semi-Supervised Learning
In ablations on DBDC4 data from 2019, our semi-supervised learning methods improve the performance of a baseline BERT model by 2\% accuracy.
DeText: A Deep Text Ranking Framework with BERT
Ranking is the most important component in a search system.
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method
Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance.
Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network
Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation.
DWM: A Decomposable Winograd Method for Convolution Acceleration
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities.
Towards Deep and Representation Learning for Talent Search at LinkedIn
In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter.
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras.
BENCHIP: Benchmarking Intelligence Processors
The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware).
Focal Track: Depth and Accommodation With Oscillating Lens Deformation
The focal track sensor is a monocular and computationally efficient depth sensor that is based on defocus controlled by a liquid membrane lens.
Towards thinner convolutional neural networks through Gradually Global Pruning
Deep network pruning is an effective method to reduce the storage and computation cost of deep neural networks when applying them to resource-limited devices.
Attribute-controlled face photo synthesis from simple line drawing
In this paper, we propose a deep generative model to synthesize face photo from simple line drawing controlled by face attributes such as hair color and complexion.
Every Filter Extracts A Specific Texture In Convolutional Neural Networks
We also demonstrate that style of images could be a combination of these texture primitives.
A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report
We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.
Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking
Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers.
Intelligent Health Recommendation System for Computer Users
The time people spend in front of computers has been increasing steadily due to the role computers play in modern society.
Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition
Firstly, we divide the feature space into several subspaces using the decomposition method proposed in this paper.
