Search Results for author:
Found 39 papers, 11 papers with code
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
Low-rank compression, a popular model compression technique that produces compact convolutional neural networks (CNNs) with low rankness, has been well-studied in the literature.
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Attention-based vision models, such as Vision Transformer (ViT) and its variants, have shown promising performance in various computer vision tasks.
Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning
Compared to the traditional augmentation method's F1-score of 64. 59%, the proposed method achieved an 18. 22% increase in the best result, demonstrating its feasibility and effectiveness in few-data industrial defect detection.
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
By interpreting automatic rank selection from an architecture search perspective, we develop an end-to-end solution to determine the suitable layer-wise ranks in a differentiable and hardware-aware way.
Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition with Hierarchical Tucker Tensor Decomposition
Meanwhile, compared with the state-of-the-art tensor decomposed model-oriented hardware TIE, our proposed FDHT-LSTM architecture achieves better performance in throughput, area efficiency and energy efficiency, respectively on LSTM-Youtube workload.
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks.
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
Specifically, we establish a heterogeneous graph that contains physical and semantic linkages to guide the feature transfer process from warmed-up video to cold-start videos.
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks
High-order decomposition is a widely used model compression approach towards compact convolutional neural networks (CNNs).
N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores
From the software perspective, we mathematically and systematically model the latency and resource utilization of the proposed heterogeneous accelerator, regarding varying system design configurations.
MUSE: Feature Self-Distillation with Mutual Information and Self-Information
Importantly, we argue and empirically demonstrate that MUSE, compared to other feature discrepancy functions, is a more functional proxy to introduce dependency and effectively improve the expressivity of all features in the knowledge distillation framework.
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network
However, the performance of these methods is limited due to the utilization of simple content-non-adaptive network parameters and the waste of the important 3D spatial information of the medical images.
GRN: Generative Rerank Network for Context-wise Recommendation
Specifically, we first design the evaluator, which applies Bi-LSTM and self-attention mechanism to model the contextual information in the labeled final ranking list and predict the interaction probability of each item more precisely.
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types.
Revisit Recommender System in the Permutation Prospective
Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN.
Safe Coupled Deep Q-Learning for Recommendation Systems
Firstly, we introduce a novel training scheme with two value functions to maximize the accumulated long-term reward under the safety constraint.
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge.
Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning
Noises, artifacts, and loss of information caused by the magnetic resonance (MR) reconstruction may compromise the final performance of the downstream applications.
Delay Constrained Buffer-Aided Relay Selection in the Internet of Things with Decision-Assisted Reinforcement Learning
This paper investigates the reinforcement learning for the relay selection in the delay-constrained buffer-aided networks.
Distant Supervision for E-commerce Query Segmentation via Attention Network
The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers.
Balanced Order Batching with Task-Oriented Graph Clustering
In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem.
EdgeRec: Recommender System on Edge in Mobile Taobao
Recommender system (RS) has become a crucial module in most web-scale applications.
Laplacian pyramid-based complex neural network learning for fast MR imaging
A Laplacian pyramid-based complex neural network, CLP-Net, is proposed to reconstruct high-quality magnetic resonance images from undersampled k-space data.
Variational Selective Autoencoder
Despite promising progress on unimodal data imputation (e. g. image inpainting), models for multimodal data imputation are far from satisfactory.
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising
The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN.
Conceptualize and Infer User Needs in E-commerce
Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications.
Learning from Partially-Observed Multimodal Data with Variational Autoencoders
Learning from only partially-observed data for imputation has been an active research area.
A Minimax Game for Instance based Selective Transfer Learning
To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning.
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
A novel query-based interactive recommender system is proposed in this paper, where \textbf{personalized questions are accurately generated from millions of automatically constructed questions} in Step 1, and \textbf{the recommendation is ensured to be closely-related to users' feedback} in Step 2.
Exact-K Recommendation via Maximal Clique Optimization
This paper targets to a novel but practical recommendation problem named exact-K recommendation.
Variational Autoencoders with Jointly Optimized Latent Dependency Structure
In particular, we express the latent variable space of a variational autoencoder (VAE) in terms of a Bayesian network with a learned, flexible dependency structure.
Multi-Modal Generative Adversarial Network for Short Product Title Generation in Mobile E-Commerce
To address this discrepancy, previous studies mainly consider textual information of long product titles and lacks of human-like view during training and evaluation process.
Product Title Refinement via Multi-Modal Generative Adversarial Learning
Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products.
Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective
With the development of dialog techniques, conversational search has attracted more and more attention as it enables users to interact with the search engine in a natural and efficient manner.
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem
A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce.
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for dialog systems.
Automatic Generation of Chinese Short Product Titles for Mobile Display
This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices.
Representing Verbs as Argument Concepts
Verbs play an important role in the understanding of natural language text.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.
