research
∙
09/08/2023
Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM
Dense SLAM based on monocular cameras does indeed have immense applicati...
share
research
∙
08/16/2023
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations
In recent years, discriminative self-supervised methods have made signif...
research
∙
06/15/2023
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Out-of-Distribution (OOD) detection is critical for the reliable operati...
research
∙
06/09/2023
FLSL: Feature-level Self-supervised Learning
Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICR...
research
∙
03/25/2023
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Building up reliable Out-of-Distribution (OOD) detectors is challenging,...
research
∙
03/16/2023
3D Masked Autoencoding and Pseudo-labeling for Domain Adaptive Segmentation of Heterogeneous Infant Brain MRI
Robust segmentation of infant brain MRI across multiple ages, modalities...
research
∙
02/08/2023
Disentangling Learning Representations with Density Estimation
Disentangled learning representations have promising utility in many app...
research
∙
01/31/2023
GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning
Multi-task learning (MTL) aims at solving multiple related tasks simulta...
research
∙
01/18/2023
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble
Ensemble learning has gain attention in resent deep learning research as...
research
∙
12/08/2022
Adaptive Neural Network Backstepping Control Method for Aerial Manipulator Based on Variable Inertia Parameter Modeling
For the aerial manipulator that performs aerial work tasks, the actual o...
research
∙
11/28/2022
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
The interaction and dimension of points are two important axes in design...
research
∙
11/11/2022
More Generalized and Personalized Unsupervised Representation Learning In A Distributed System
Discriminative unsupervised learning methods such as contrastive learnin...
research
∙
11/06/2022
Preserving background sound in noise-robust voice conversion via multi-task learning
Background sound is an informative form of art that is helpful in provid...
research
∙
10/02/2022
Approximate Computing and the Efficient Machine Learning Expedition
Approximate computing (AxC) has been long accepted as a design alternati...
research
∙
09/30/2022
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Due to limited communication capacities of edge devices, most existing f...
research
∙
09/21/2022
NashAE: Disentangling Representations through Adversarial Covariance Minimization
We present a self-supervised method to disentangle factors of variation ...
research
∙
09/09/2022
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification
Machine learning methods must be trusted to make appropriate decisions i...
research
∙
09/08/2022
FADE: Enabling Large-Scale Federated Adversarial Training on Resource-Constrained Edge Devices
Adversarial Training (AT) has been proven to be an effective method of i...
research
∙
09/08/2022
Joint Optimization of STAR-RIS Assisted UAV Communication Systems
In this letter, we study the simultaneously transmitting and reflecting ...
research
∙
08/26/2022
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming
We consider learning and compositionality as the key mechanisms towards ...
research
∙
08/23/2022
Self-Trained Proposal Networks for the Open World
Deep learning-based object proposal methods have enabled significant adv...
research
∙
08/21/2022
Vox-Surf: Voxel-based Implicit Surface Representation
Virtual content creation and interaction play an important role in moder...
research
∙
07/14/2022
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
The rise of deep neural networks provides an important driver in optimiz...
research
∙
02/21/2022
Privacy Leakage of Adversarial Training Models in Federated Learning Systems
Adversarial Training (AT) is crucial for obtaining deep neural networks ...
research
∙
11/17/2021
ReaLPrune: ReRAM Crossbar-aware Lottery Ticket Pruned CNNs
Training machine learning (ML) models at the edge (on-chip training on e...
research
∙
10/26/2021
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
Federated learning (FL) is a popular distributed learning framework that...
research
∙
10/23/2021
A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness
Neural networks are getting better accuracy with higher energy and compu...
research
∙
10/10/2021
NViT: Vision Transformer Compression and Parameter Redistribution
Transformers yield state-of-the-art results across many tasks. However, ...
research
∙
07/03/2021
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
Learning with graphs has attracted significant attention recently. Exist...
research
∙
06/07/2021
Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure
Enabling out-of-distribution (OOD) detection for DNNs is critical for th...
research
∙
04/21/2021
Neuromorphic Algorithm-hardware Codesign for Temporal Pattern Learning
Neuromorphic computing and spiking neural networks (SNN) mimic the behav...
research
∙
03/17/2021
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
We design blackbox transfer-based targeted adversarial attacks for an en...
research
∙
03/17/2021
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
Over recent years, a myriad of novel convolutional network architectures...
research
∙
03/02/2021
A Case for 3D Integrated System Design for Neuromorphic Computing AI Applications
Over the last decade, artificial intelligence has found many application...
research
∙
02/20/2021
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Mixed-precision quantization can potentially achieve the optimal tradeof...
research
∙
01/19/2021
On Provable Backdoor Defense in Collaborative Learning
As collaborative learning allows joint training of a model using multipl...
research
∙
12/29/2020
Improving Adversarial Robustness in Weight-quantized Neural Networks
Neural networks are getting deeper and more computation-intensive nowada...
research
∙
12/08/2020
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
Federated learning (FL) is a popular distributed learning framework that...
research
∙
12/08/2020
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
Graph-based semi-supervised node classification (GraphSSC) has wide appl...
research
∙
11/30/2020
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
Scale variance among different sizes of body parts and objects is a chal...
research
∙
11/26/2020
Fast IR Drop Estimation with Machine Learning
IR drop constraint is a fundamental requirement enforced in almost all c...
research
∙
11/06/2020
Low-Cost Floating-Point Processing in ReRAM for Scientific Computing
We propose ReFloat, a principled approach for low-cost floating-point pr...
research
∙
10/29/2020
The IQIYI System for Voice Conversion Challenge 2020
This paper presents the IQIYI voice conversion system (T24) for Voice Co...
research
∙
09/30/2020
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
Recent research finds CNN models for image classification demonstrate ov...
research
∙
09/01/2020
Evasion Attacks to Graph Neural Networks via Influence Function
Graph neural networks (GNNs) have achieved state-of-the-art performance ...
research
∙
09/01/2020
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs
Link prediction in dynamic graphs (LPDG) is an important research proble...
research
∙
08/07/2020
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Federated learning is a popular distributed machine learning paradigm wi...
research
∙
07/08/2020
NASGEM: Neural Architecture Search via Graph Embedding Method
Neural Architecture Search (NAS) automates and prospers the design of ne...
research
∙
06/12/2020
Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
Deepfake represents a category of face-swapping attacks that leverage ma...
research
∙
05/14/2020
