Search Results for author:
Found 51 papers, 18 papers with code
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations.
Invariant Test-Time Adaptation for Vision-Language Model Generalization
Vision-language foundation models have exhibited remarkable success across a multitude of downstream tasks due to their scalability on extensive image-text paired datasets.
LLM Inference Unveiled: Survey and Roofline Model Insights
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques.
Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping
To further exploit the capabilities of bootstrapping, we investigate and adjust the training order of data, which yields improved performance of the model.
Rethinking and Simplifying Bootstrapped Graph Latents
Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations.
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.
Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale
The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products.
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators
Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge.
Adapting Large Language Models for Content Moderation: Pitfalls in Data Engineering and Supervised Fine-tuning
Specifically, we discuss the differences between discriminative and generative models using content moderation as an example.
VDC: Versatile Data Cleanser based on Visual-Linguistic Inconsistency by Multimodal Large Language Models
The role of data in building AI systems has recently been emphasized by the emerging concept of data-centric AI.
Is GPT4 a Good Trader?
Recently, large language models (LLMs), particularly GPT-4, have demonstrated significant capabilities in various planning and reasoning tasks \cite{cheng2023gpt4, bubeck2023sparks}.
SAILOR: Structural Augmentation Based Tail Node Representation Learning
In the pursuit of promoting the expressiveness of GNNs for tail nodes, we explore how the deficiency of structural information deteriorates the performance of tail nodes and propose a general Structural Augmentation based taIL nOde Representation learning framework, dubbed as SAILOR, which can jointly learn to augment the graph structure and extract more informative representations for tail nodes.
Semantic Equivariant Mixup
Then a generic mixup regularization at the representation level is proposed, which can further regularize the model with the semantic information in mixed samples.
Proceedings of the 40th International Conference on Machine Learning
Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted.
Uncertainty in Natural Language Processing: Sources, Quantification, and Applications
Therefore, in this survey, we provide a comprehensive review of uncertainty-relevant works in the NLP field.
Calibrating Multimodal Learning
Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted.
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments.
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?
The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning.
Reweighted Mixup for Subpopulation Shift
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions.
SLPerf: a Unified Framework for Benchmarking Split Learning
Data privacy concerns has made centralized training of data, which is scattered across silos, infeasible, leading to the need for collaborative learning frameworks.
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.
Federated Nearest Neighbor Machine Translation
To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.
Post-training Quantization on Diffusion Models
These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise.
Learning with Noisy Labels over Imbalanced Subpopulations
Then, we refurbish the noisy labels using the estimated clean probabilities and the pseudo-labels from the model's predictions.
Vertical Federated Linear Contextual Bandits
In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i. e., contextual information is vertically distributed over different departments.
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD).
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
Recently, there has been a growing surge of interest in enabling machine learning systems to generalize well to Out-of-Distribution (OOD) data.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
In this paper, we propose a general framework to solve the above two challenges simultaneously.
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
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.
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
Second, the overall design space composed of HW/SW partitioning, hardware optimization, and software optimization is huge.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
ASFGNN: Automated Separated-Federated Graph Neural Network
Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.
S3ML: A Secure Serving System for Machine Learning Inference
We present S3ML, a secure serving system for machine learning inference in this paper.
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
The inference of a input sample can exit from early stage if the prediction of the stage is confident enough.
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
Industrial Scale Privacy Preserving Deep Neural Network
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.
A Survey of Adversarial Learning on Graphs
To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.
Practical Privacy Preserving POI Recommendation
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
Secure Social Recommendation based on Secret Sharing
It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems.
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN).
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Specifically, we use homomorphic encryption to protect a client's raw data and use Bayesian neural networks to protect the DNN weights in a cloud server.
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
(2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset.
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution
In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks.
