Search Results for author:
Found 47 papers, 7 papers with code
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
Face Analysis Project on MXNet
Ranked #5 on
Lightweight Face Recognition
on AgeDB-30
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
However, the retrieval-based methods are sub-optimal and would cause more or less information losses, and it's difficult to balance the effectiveness and efficiency of the retrieval algorithm.
ML-Bench: Evaluating Large Language Models for Code Generation in Repository-Level Machine Learning Tasks
While Large Language Models (LLMs) have demonstrated proficiency in code generation benchmarks, translating these results into practical development scenarios - where leveraging existing repository-level libraries is the norm - remains challenging.
Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems
To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise.
MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.
Exploring Post-Training Quantization of Protein Language Models
In summary, our study introduces an innovative PTQ method for ProteinLMs, addressing specific quantization challenges and potentially leading to the development of more efficient ProteinLMs with significant implications for various protein-related applications.
Physics-Guided Generative Adversarial Networks for Sea Subsurface Temperature Prediction
Physical modeling methods can offer the potential for extrapolation beyond observational conditions, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns.
Sparse Linear Isotonic Models
In machine learning and data mining, linear models have been widely used to model the response as parametric linear functions of the predictors.
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work.
Alternating Estimation for Structured High-Dimensional Multi-Response Models
We consider learning high-dimensional multi-response linear models with structured parameters.
Structured Matrix Recovery via the Generalized Dantzig Selector
In recent years, structured matrix recovery problems have gained considerable attention for its real world applications, such as recommender systems and computer vision.
Estimation with Norm Regularization
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model.
l1-norm Penalized Orthogonal Forward Regression
A l1-norm penalized orthogonal forward regression (l1-POFR) algorithm is proposed based on the concept of leaveone- out mean square error (LOOMSE).
Generalized Dantzig Selector: Application to the k-support norm
For statistical analysis, we provide upper bounds for the Gaussian widths needed in the GDS analysis, yielding the first statistical recovery guarantee for estimation with the $k$-support norm.
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer.
Inferring Remote Channel State Information: Cramér-Rao Lower Bound and Deep Learning Implementation
Channel state information (CSI) is of vital importance in wireless communication systems.
Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks
In this paper, we propose a learning-based low-overhead beam alignment method for vehicle-to-infrastructure communication in vehicular networks.
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach
Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions.
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression
To find the coefficient vector, estimators with a joint approximation of the noise covariance are often preferred than the simple linear regression in view of their superior empirical performance, which can be generally solved by alternating-minimization type procedures.
Structured Estimation with Atomic Norms: General Bounds and Applications
For structured estimation problems with atomic norms, recent advances in the literature express sample complexity and estimation error bounds in terms of certain geometric measures, in particular Gaussian width of the unit norm ball, Gaussian width of a spherical cap induced by a tangent cone, and a restricted norm compatibility constant.
Robust Structured Estimation with Single-Index Models
In this paper, we investigate general single-index models (SIMs) in high dimensions.
Multi-Object Tracking via Constrained Sequential Labeling
This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations.
Person Count Localization in Videos From Noisy Foreground and Detections
This problem is a middle-ground between frame-level person counting, which does not localize counts, and person detection aimed at perfectly localizing people with count-one detections.
DeepSquare: Boosting the Learning Power of Deep Convolutional Neural Networks with Elementwise Square Operators
In this paper, we pursue very efficient neural network modules which can significantly boost the learning power of deep convolutional neural networks with negligible extra computational cost.
Research on a New Convolutional Neural Network Model Combined with Random Edges Adding
Based on the idea of small world network, a random edge adding algorithm is proposed to improve the performance of convolutional neural network model.
Quantum Criticism: A Tagged News Corpus Analysed for Sentiment and Named Entities
In this research, we continuously collect data from the RSS feeds of traditional news sources.
Towards Playing Full MOBA Games with Deep Reinforcement Learning
However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings
Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.
Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search
Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome.
Gaussian Dynamic Convolution for Efficient Single-Image Segmentation
Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation.
Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG
On the DMS data set, GF-DANN has obtained an accuracy rate of 89. 47%, and the sensitivity and specificity are 90% and 89%.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series
For structure parameters, the kernel dictionary is selected by some sparsification techniques with online selective modeling criteria, and moreover the kernel covariance matrix is intermittently optimized in the light of the covariance matrix adaptation evolution strategy (CMA-ES).
CLIP2TV: Align, Match and Distill for Video-Text Retrieval
Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.
Ranked #11 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution
Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint using counterfactual predictions.
Transformer-Empowered 6G Intelligent Networks: From Massive MIMO Processing to Semantic Communication
It is anticipated that 6G wireless networks will accelerate the convergence of the physical and cyber worlds and enable a paradigm-shift in the way we deploy and exploit communication networks.
AutoFAS: Automatic Feature and Architecture Selection for Pre-Ranking System
Industrial search and recommendation systems mostly follow the classic multi-stage information retrieval paradigm: matching, pre-ranking, ranking, and re-ranking stages.
Contrastive Information Transfer for Pre-Ranking Systems
In this paper, we focus on the information transfer from ranking to pre-ranking stage.
A Novel DeBERTa-based Model for Financial Question Answering Task
As a rising star in the field of natural language processing, question answering systems (Q&A Systems) are widely used in all walks of life.
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation.
Joint Block-Sparse Recovery Using Simultaneous BOMP/BOLS
We consider the greedy algorithms for the joint recovery of high-dimensional sparse signals based on the block multiple measurement vector (BMMV) model in compressed sensing (CS).
Precise Few-shot Fat-free Thigh Muscle Segmentation in T1-weighted MRI
However, due to the insufficient amount of precise annotations, thigh muscle masks generated by deep learning approaches tend to misclassify intra-muscular fat (IMF) as muscle impacting the analysis of muscle volumetrics.
SPM: Structured Pretraining and Matching Architectures for Relevance Modeling in Meituan Search
At pretraining stage, we propose an effective pretraining method that employs both query and multiple fields of document as inputs, including an effective information compression method for lengthy fields.
One-for-All: Towards Universal Domain Translation with a Single StyleGAN
In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences.
Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning
For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel.
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation
Specifically, our strategy considers both forward and backward adaptation, to transfer the forgery knowledge from the source domain to the target domain in forward adaptation and then reverse the adaptation from the target domain to the source domain in backward adaptation.
Block-Sparse Tensor Recovery
This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment.
