Search Results for author:
Found 31 papers, 14 papers with code
Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting
To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA.
MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation
Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders.
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection
Drawing inspiration from Psychological Questionnaires, which are carefully designed by psychologists to evaluate individual personality traits through a series of targeted items, we argue that these items can be regarded as a collection of well-structured chain-of-thought (CoT) processes.
Privacy-Preserving Face Recognition Using Random Frequency Components
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals.
RPTQ: Reorder-based Post-training Quantization for Large Language Models
In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers.
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures.
Towards Stable Test-Time Adaptation in Dynamic Wild World
In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability.
Probabilistic Knowledge Distillation of Face Ensembles
Mean ensemble (i. e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model.
Quantized Adaptive Subgradient Algorithms and Their Applications
Therefore, a threshold quantization strategy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model.
Privacy-Preserving Face Recognition with Learnable Privacy Budgets in Frequency Domain
This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain.
Evaluation-oriented Knowledge Distillation for Deep Face Recognition
Inspired by the ultimate goal of KD methods, we propose a novel Evaluation oriented KD method (EKD) for deep face recognition to directly reduce the performance gap between the teacher and student models during training.
Multi-scale Attention Flow for Probabilistic Time Series Forecasting
The probability prediction of multivariate time series is a notoriously challenging but practical task.
Efficient Test-Time Model Adaptation without Forgetting
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w. r. t.
Boost Test-Time Performance with Closed-Loop Inference
Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance.
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
AdaXpert: Adapting Neural Architecture for Growing Data
To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data.
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index.
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models
In this paper, we propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.
tFold-TR: Combining Deep Learning Enhanced Hybrid Potential Energy for Template-Based Modeling Structure Refinement
Protein structure prediction has been a grand challenge for over 50 years, owing to its broad scientific and application interests.
Federated Face Recognition
Face recognition has been extensively studied in computer vision and artificial intelligence communities in recent years.
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Nevertheless, it is unclear how parameter sharing affects the searching process.
Real-Time Semantic Segmentation via Auto Depth, Downsampling Joint Decision and Feature Aggregation
To satisfy the stringent requirements on computational resources in the field of real-time semantic segmentation, most approaches focus on the hand-crafted design of light-weight segmentation networks.
Neural Architecture Search
Real-Time Semantic Segmentation
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Disturbance-immune Weight Sharing for Neural Architecture Search
To alleviate the performance disturbance issue, we propose a new disturbance-immune update strategy for model updating.
Few Shot Network Compression via Cross Distillation
The core challenge of few shot network compression lies in high estimation errors from the original network during inference, since the compressed network can easily over-fits on the few training instances.
An Efficient Approach to Informative Feature Extraction from Multimodal Data
We further generalize the framework to handle more than two modalities and missing modalities.
PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural Networks
Deep neural networks are widely used in various domains, but the prohibitive computational complexity prevents their deployment on mobile devices.
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Large-scale distributed optimization is of great importance in various applications.
Double Quantization for Communication-Efficient Distributed Optimization
In this paper, to reduce the communication complexity, we propose \emph{double quantization}, a general scheme for quantizing both model parameters and gradients.
Quantized Convolutional Neural Networks for Mobile Devices
Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks.
Online Sketching Hashing
Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention.
Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction
Image matching is one of the most challenging stages in 3D reconstruction, which usually occupies half of computational cost and inaccurate matching may lead to failure of reconstruction.
