Search Results for author:
Found 98 papers, 36 papers with code
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes
Despite recent advancements in automated speech recognition (ASR) technologies, reports of unequal performance across speakers of different demographic groups abound.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization
In this paper, we present an adversarial meta-evaluation methodology that allows us to (i) diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets, (ii) search for directions for further improvement by data augmentation.
Towards Building the Federated GPT: Federated Instruction Tuning
This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples
We propose a practical vertical federated learning (VFL) framework called \textbf{one-shot VFL} that can solve the communication bottleneck and the problem of limited overlapping samples simultaneously based on semi-supervised learning.
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties
In this paper, we propose \textbf{Party-wise Dropout} to improve the VFL model's robustness against the unexpected exit of passive parties and a defense method called \textbf{DIMIP} to protect the active party's IP in the deployment phase.
SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection
Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training.
PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration
To address this challenge, we propose PowerPruning, a novel method to reduce power consumption in digital neural network accelerators by selecting weights that lead to less power consumption in MAC operations.
Analog, In-memory Compute Architectures for Artificial Intelligence
This paper presents an analysis of the fundamental limits on energy efficiency in both digital and analog in-memory computing architectures, and compares their performance to single instruction, single data (scalar) machines specifically in the context of machine inference.
Biologically Plausible Learning on Neuromorphic Hardware Architectures
Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip.
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data.
Ranked #6 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Neural Architecture Search
Robust 3D Semantic Segmentation
+1
More Generalized and Personalized Unsupervised Representation Learning In A Distributed System
In this work, we propose a novel method, FedStyle, to learn a more generalized global model by infusing local style information with local content information for contrastive learning, and to learn more personalized local models by inducing local style information for downstream tasks.
Rethinking Normalization Methods in Federated Learning
We also show that layer normalization is a better choice in FL which can mitigate the external covariate shift and improve the performance of the global model.
Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer
Developing neural architectures that are capable of logical reasoning has become increasingly important for a wide range of applications (e. g., natural language processing).
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction
Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees.
Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification
Furthermore, we diagnose the classifiers performance at each level of the hierarchy improving the explainability and interpretability of the models predictions.
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices
However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices.
Self-Trained Proposal Networks for the Open World
Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes.
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.
The Dark Side: Security Concerns in Machine Learning for EDA
The growing IC complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies.
Privacy Leakage of Adversarial Training Models in Federated Learning Systems
Adversarial Training (AT) is crucial for obtaining deep neural networks that are robust to adversarial attacks, yet recent works found that it could also make models more vulnerable to privacy attacks.
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance
We therefore propose HERO, a Hessian-enhanced robust optimization method, to minimize the Hessian eigenvalues through a gradient-based training process, simultaneously improving the generalization and quantization performance.
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
Furthermore, we derive a certified robustness guarantee against model poisoning attacks and a convergence guarantee to FedAvg after applying our FL-WBC.
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor
MAKPConv employs a depthwise kernel to reduce resource consumption and re-calibrates the contribution of kernel points towards each neighbor point via Neighbor-Kernel attention to improve representation power.
MTG: A Benchmark Suite for Multilingual Text Generation
We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation.
Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation
Moreover, the design houses are also unwilling to directly share such data with the other houses to build a unified model, which can be ineffective for the design house with unique design patterns due to data insufficiency.
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective
Existing representation learning methods on graphs have achieved state-of-the-art performance on various graph-related tasks such as node classification, link prediction, etc.
Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective
The key idea of our defense is learning to perturb data representation such that the quality of the reconstructed data is severely degraded, while FL performance is maintained.
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments
We then propose Mixture Outlier Exposure (MixOE), which mixes ID data and training outliers to expand the coverage of different OOD granularities, and trains the model such that the prediction confidence linearly decays as the input transitions from ID to OOD.
Medical Image Classification
Out-of-Distribution Detection
+1
Enhancing Scientific Papers Summarization with Citation Graph
Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network.
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning
In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL.
Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
During the online phase of the attack, we then leverage representations of highly related proxy classes from the whitebox distribution to fool the blackbox model into predicting the desired target class.
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
Over recent years, a myriad of novel convolutional network architectures have been developed to advance state-of-the-art performance on challenging recognition tasks.
BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
Mixed-precision quantization can potentially achieve the optimal tradeoff between performance and compression rate of deep neural networks, and thus, have been widely investigated.
On Provable Backdoor Defense in Collaborative Learning
The framework shows that the subset selection process, a deciding factor for subset aggregation methods, can be viewed as a code design problem.
Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective
In this work, we show our key observation that the data representation leakage from gradients is the essential cause of privacy leakage in FL.
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs
However, existing FL methods 1) perform poorly when data across clients are non-IID, 2) cannot handle data with new label domains, and 3) cannot leverage unlabeled data, while all these issues naturally happen in real-world graph-based problems.
Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition
We use ScaleNAS to create high-resolution models for two different tasks, ScaleNet-P for human pose estimation and ScaleNet-S for semantic segmentation.
Ranked #5 on
Multi-Person Pose Estimation
on COCO test-dev
Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation
Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow.
Fast IR Drop Estimation with Machine Learning
IR drop constraint is a fundamental requirement enforced in almost all chip designs.
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work.
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
Moreover, the proposed CNN model is general and transferable to different designs.
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems
In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora.
Evasion Attacks to Graph Neural Networks via Influence Function
Next, we reformulate the evasion attack against GNNs to be related to calculating label influence on LP, which is applicable to multi-layer GNNs and does not need to know the GNN model.
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs
Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational studies, etc.
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets
Rather than learning a shared global model in classic federated learning, each client learns a personalized model via LotteryFL; the communication cost can be significantly reduced due to the compact size of lottery networks.
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training
In this paper, \textit{SparseTrain} is proposed to accelerate CNN training by fully exploiting the sparsity.
NASGEM: Neural Architecture Search via Graph Embedding Method
To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method.
Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces
On the one hand, the quality of the synthesized faces is reduced with more visual artifacts such that the synthesized faces are more obviously fake or less convincing to human observers.
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
Instead of performing stylization frame by frame, only key frames in the original video are processed by a pre-trained deep neural network (DNN) on edge servers, while the rest of stylized intermediate frames are generated by our designed optical-flow-based frame interpolation algorithm on mobile phones.
TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations
The goal of this framework is to learn a feature extractor that can hide the privacy information from the intermediate representations; while maximally retaining the original information embedded in the raw data for the data collector to accomplish unknown learning tasks.
PENNI: Pruned Kernel Sharing for Efficient CNN Inference
Although state-of-the-art (SOTA) CNNs achieve outstanding performance on various tasks, their high computation demand and massive number of parameters make it difficult to deploy these SOTA CNNs onto resource-constrained devices.
TRP: Trained Rank Pruning for Efficient Deep Neural Networks
The TRP trained network inherently has a low-rank structure, and is approximated with negligible performance loss, thus eliminating the fine-tuning process after low rank decomposition.
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability
We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers.
Transferable Perturbations of Deep Feature Distributions
Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network.
Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification
In this work, we propose SVD training, the first method to explicitly achieve low-rank DNNs during training without applying SVD on every step.
Extractive Summarization as Text Matching
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Ranked #1 on
Text Summarization
on BBC XSum
Neural Predictor for Neural Architecture Search
First we train N random architectures to generate N (architecture, validation accuracy) pairs and use them to train a regression model that predicts accuracy based on the architecture.
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1. 5 GPU hours, which is 7. 2x and 6. 7x faster than the crafting time of SOTA CNN and RNN models, respectively.
Structural sparsification for Far-field Speaker Recognition with GNA
Recently, deep neural networks (DNN) have been widely used in speaker recognition area.
Trained Rank Pruning for Efficient Deep Neural Networks
To accelerate DNNs inference, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations.
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training
As such, training our GAN architecture requires much fewer high-quality images with a small number of additional low-quality images.
Towards Efficient and Secure Delivery of Data for Deep Learning with Privacy-Preserving
When using MoLe for VGG-16 network on CIFAR dataset, the computational overhead is only 9% and the data transmission overhead is 5. 12%.
DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones
Our experiments on CelebA and LFW datasets show that the quality of the reconstructed images from the obfuscated features of the raw image is dramatically decreased from 0. 9458 to 0. 3175 in terms of multi-scale structural similarity.
Accelerating CNN Training by Pruning Activation Gradients
Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed.
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost.
eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform
Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation.
Snooping Attacks on Deep Reinforcement Learning
In these snooping threat models, the adversary does not have the ability to interact with the target agent's environment, and can only eavesdrop on the action and reward signals being exchanged between agent and environment.
Integral Pruning on Activations and Weights for Efficient Neural Networks
With the rapidly scaling up of deep neural networks (DNNs), extensive research studies on network model compression such as weight pruning have been performed for efficient deployment.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems with low latency and power.
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
In this paper, inspired by recent work in machine learning systems, we propose a solution HyPar to determine layer-wise parallelism for deep neural network training with an array of DNN accelerators.
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
We intuitively and empirically prove the rationality of our method in reducing the search space.
Trained Rank Pruning for Efficient Deep Neural Networks
We propose Trained Rank Pruning (TRP), which iterates low rank approximation and training.
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
The success of deep learning research has catapulted deep models into production systems that our society is becoming increasingly dependent on, especially in the image and video domains.
LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning
Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time.
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Thus judiciously selecting different precision for different layers/structures can potentially produce more efficient models compared to traditional quantization methods by striking a better balance between accuracy and compression rate.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
Generalized Inverse Optimization through Online Learning
Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs.
Towards Efficient and Secure Delivery of Data for Training and Inference with Privacy-Preserving
When using MoLe for VGG-16 network on CIFAR dataset, the computational overhead is only 9% and the data transmission overhead is 5. 12%.
2PFPCE: Two-Phase Filter Pruning Based on Conditional Entropy
To overcome this challenge, we propose a novel filter-pruning framework, two-phase filter pruning based on conditional entropy, namely \textit{2PFPCE}, to compress the CNN models and reduce the inference time with marginal performance degradation.
DPatch: An Adversarial Patch Attack on Object Detectors
Successful realization of DPatch also illustrates the intrinsic vulnerability of the modern detector architectures to such patch-based adversarial attacks.
SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning
It becomes an open question whether escaping sharp minima can improve the generalization.
Exploiting Spin-Orbit Torque Devices as Reconfigurable Logic for Circuit Obfuscation
Circuit obfuscation is a frequently used approach to conceal logic functionalities in order to prevent reverse engineering attacks on fabricated chips.
Emerging Technologies Cryptography and Security
Spintronics based Stochastic Computing for Efficient Bayesian Inference System
Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness.
Learning Intrinsic Sparse Structures within Long Short-Term Memory
This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs.
GraphR: Accelerating Graph Processing Using ReRAM
GRAPHR gains a speedup of 1. 16x to 4. 12x, and is 3. 67x to 10. 96x more energy efficiency compared to PIM-based architecture.
Distributed, Parallel, and Cluster Computing Hardware Architecture
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients.
Coordinating Filters for Faster Deep Neural Networks
Moreover, Force Regularization better initializes the low-rank DNNs such that the fine-tuning can converge faster toward higher accuracy.
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Our DNN has 4. 1M parameters, which is only 6. 7% of AlexNet or 59% of GoogLeNet.
Generative Poisoning Attack Method Against Neural Networks
A countermeasure is also designed to detect such poisoning attack methods by checking the loss of the target model.
Learning Structured Sparsity in Deep Neural Networks
SSL can: (1) learn a compact structure from a bigger DNN to reduce computation cost; (2) obtain a hardware-friendly structured sparsity of DNN to efficiently accelerate the DNNs evaluation.
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Pruning CNNs in a way that increase inference speed often imposes specific sparsity structures, thus limiting the achievable sparsity levels.
A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip
IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency.
