Search Results for author:
Found 87 papers, 34 papers with code
xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem
In this paper, we use Clique Partition- ing Problem (CPP), which is an Integer Pro- gram (IP) to formulate ER as a graph partition- ing problem and then highlight the explainable nature of this method.
A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale
Our analysis further reveal stereotypical portrayals of both male and female characters in terms of moral foundations and events.
Submission-Aware Reviewer Profiling for Reviewer Recommender System
We propose an approach that learns from each abstract published by a potential reviewer the topics studied and the explicit context in which the reviewer studied the topics.
Extensible Proxy for Efficient NAS
(2) Efficient proxies are not extensible to multi-modality downstream tasks.
Can Language Models Be Specific? How?
In this paper, we propose to measure how specific the language of pre-trained language models (PLMs) is.
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a source concept analogous to a given target concept (aka Analogous Concept Generation or ACG), and generating an explanation of the similarity between a given pair of target concept and source concept (aka Analogous Explanation Generation or AEG).
HiKonv: Maximizing the Throughput of Quantized Convolution With Novel Bit-wise Management and Computation
In this study, we propose HiKonv, a unified solution that maximizes the throughput of convolution on a given underlying processing unit with low-bitwidth quantized data inputs through novel bit-wise management and parallel computation.
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data.
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration
The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries.
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation
Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs.
Understanding Jargon: Combining Extraction and Generation for Definition Modeling
From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge.
Graph Neural Network Training with Data Tiering
With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead.
MLHarness: A Scalable Benchmarking System for MLCommons
With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common set of measures for ML/DL models with large scale open datasets under common development practices and resources so that people can benchmark and compare models quality and performance on a common ground.
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer.
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow
Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise.
Open Relation Modeling: Learning to Define Relations between Entities
Relations between entities can be represented by different instances, e. g., a sentence containing both entities or a fact in a Knowledge Graph (KG).
Generic Neural Architecture Search via Regression
Such a self-supervised regression task can effectively evaluate the intrinsic power of an architecture to capture and transform the input signal patterns, and allow more sufficient usage of training samples.
Ranked #1 on
Neural Architecture Search
on NAS-Bench-101
Global Rhythm Style Transfer Without Text Transcriptions
In this paper, we propose AutoPST, which can disentangle global prosody style from speech without relying on any text transcriptions.
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach
To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain.
Heterogeneous Contrastive Learning
We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue.
Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection
Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union~(IoU) metric.
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives.
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture
In this work, we propose a novel GPU-oriented data communication approach for GCN training, where GPU threads directly access sparse features in host memory through zero-copy accesses without much CPU help.
PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accesses
While this process accounts for a significant portion of the training time, we find existing GNN implementations using popular deep neural network (DNN) libraries such as PyTorch are limited to a CPU-centric approach for the entire data preparation step.
Improving Random-Sampling Neural Architecture Search by Evolving the Proxy Search Space
This raises the question of whether we can find an effective proxy search space (PS) that is only a small subset of GS to dramatically improve RandomNAS’s search efficiency while at the same time keeping a good correlation for the top-performing architectures.
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
Moreover, as the algorithm for training a sparse neural network is specified as (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned model weights in the hidden layer.
TEMPI: An Interposed MPI Library with a Canonical Representation of CUDA-aware Datatypes
MPI derived datatypes are an abstraction that simplifies handling of non-contiguous data in MPI applications.
Distributed, Parallel, and Cluster Computing
When Machine Learning Meets Quantum Computers: A Case Study
It is imminent to know how to design the quantum circuit for accelerating neural networks.
Interpretable Visual Reasoning via Induced Symbolic Space
Finally, we achieve a higher level of interpretability by imposing OCCAM on the objects represented in the induced symbolic concept space.
Ranked #3 on
Visual Question Answering (VQA)
on CLEVR
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
High quality AI solutions require joint optimization of AI algorithms, such as deep neural networks (DNNs), and their hardware accelerators.
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack).
At-Scale Sparse Deep Neural Network Inference with Efficient GPU Implementation
This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020.
ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation
Real-time cine magnetic resonance imaging (MRI) plays an increasingly important role in various cardiac interventions.
A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage
We discover that, in order to make full use of the strength of quantum representation, it is best to represent data in a neural network as either random variables or numbers in unitary matrices, such that they can be directly operated by the basic quantum logical gates.
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.
A Multi-Perspective Architecture for Semantic Code Search
The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories.
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions
We formulate the co-search problem by fusing DNN search variables and hardware implementation variables into one solution space, and maximize both algorithm accuracy and hardware implementation quality.
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i. e. reducing domain shift.
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.
Ranked #8 on
Semantic Segmentation
on DensePASS
Multi-Cycle-Consistent Adversarial Networks for CT Image Denoising
For example, if the noise is large leading to significant difference between domain $X$ and domain $Y$, can we bridge $X$ and $Y$ with an intermediate domain $Z$ such that both the denoising process between $X$ and $Z$ and that between $Z$ and $Y$ are easier to learn?
DLSpec: A Deep Learning Task Exchange Specification
Deep Learning (DL) innovations are being introduced at a rapid pace.
MLModelScope: A Distributed Platform for Model Evaluation and Benchmarking at Scale
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.
Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation
Existing selective segmentation methods, however, ignore this unique property of selective segmentation and train their DNN models by optimizing accuracy on the entire dataset.
On Interpretability of Artificial Neural Networks: A Survey
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success in many important areas that deal with text, images, videos, graphs, and so on.
Tensor Recovery from Noisy and Multi-Level Quantized Measurements
Existing works on tensor recovery have focused on data losses and random noises.
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration
In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue.
Distributed, Parallel, and Cluster Computing
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
The Design and Implementation of a Scalable DL Benchmarking Platform
MLModelScope defines abstractions for frameworks and supports board range of DL models and evaluation scenarios.
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving
The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations.
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)
We show that DLBricks provides an accurate performance estimate for the DL models and reduces the benchmarking time across systems (e. g. within $95\%$ accuracy and up to $4. 4\times$ benchmarking time speedup on Amazon EC2 c5. xlarge).
Benanza: Automatic $μ$Benchmark Generation to Compute "Lower-bound" Latency and Inform Optimizations of Deep Learning Models on GPUs
An important venue for such improvement is to profile the execution of these models and characterize their performance to identify possible optimization opportunities.
Faceted Hierarchy: A New Graph Type to Organize Scientific Concepts and a Construction Method
On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts.
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space
Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers.
MLModelScope: A Distributed Platform for ML Model Evaluation and Benchmarking at Scale
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them.
CNAS: Channel-Level Neural Architecture Search
There is growing interest in automating designing good neural network architectures.
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Object detection and tracking are challenging tasks for resource-constrained embedded systems.
MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation
Cardiac magnetic resonance imaging (MRI) is an essential tool for MRI-guided surgery and real-time intervention.
SPGNet: Semantic Prediction Guidance for Scene Parsing
The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path.
XSP: Across-Stack Profiling and Analysis of Machine Learning Models on GPUs
Such an endeavor is challenging as the characteristics of an ML model depend on the interplay between the model, framework, system libraries, and the hardware (or the HW/SW stack).
Equipping Educational Applications with Domain Knowledge
One of the challenges of building natural language processing (NLP) applications for education is finding a large domain-specific corpus for the subject of interest (e. g., history or science).
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
Developing artificial intelligence (AI) at the edge is always challenging, since edge devices have limited computation capability and memory resources but need to meet demanding requirements, such as real-time processing, high throughput performance, and high inference accuracy.
A Retrospective Recount of Computer Architecture Research with a Data-Driven Study of Over Four Decades of ISCA Publications
Recently we decided to conduct a more thorough study based on all past papers of International Symposium on Computer Architecture (ISCA) from 1973 to 2018, which resulted this article.
A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices
Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption.
Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking
An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed - leading to a diverse landscape of ML.
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge
While embedded FPGAs are attractive platforms for DNN acceleration on edge-devices due to their low latency and high energy efficiency, the scarcity of resources of edge-scale FPGA devices also makes it challenging for DNN deployment.
Reinforcement Learning Based Text Style Transfer without Parallel Training Corpus
Our generator employs an attention-based encoder-decoder to transfer a sentence from the source style to the target style.
Document Similarity for Texts of Varying Lengths via Hidden Topics
This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information.
SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection
By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models.
Revisiting the Evaluation of Uncertainty Estimation and Its Application to Explore Model Complexity-Uncertainty Trade-Off
Accurately estimating uncertainties in neural network predictions is of great importance in building trusted DNNs-based models, and there is an increasing interest in providing accurate uncertainty estimation on many tasks, such as security cameras and autonomous driving vehicles.
Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era
We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.
Frustrated with Replicating Claims of a Shared Model? A Solution
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them.
TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments
Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines.
Distributed, Parallel, and Cluster Computing
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization
While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in an easy-to-hard manner and on the selected examples progressively.
Learning Motion in Feature Space: Locally-Consistent Deformable Convolution Networks for Fine-Grained Action Detection
Fine-grained action detection is an important task with numerous applications in robotics and human-computer interaction.
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
In particular, DCR places a separate classification network in parallel with the localization network (base detector).
SCOPE: C3SR Systems Characterization and Benchmarking Framework
This report presents the design of the Scope infrastructure for extensible and portable benchmarking.
Performance
Universal Approximation with Quadratic Deep Networks
(4) To approximate the same class of functions with the same error bound, is a quantized quadratic network able to enjoy a lower number of weights than a quantized conventional network?
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C).
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Textual grounding is an important but challenging task for human-computer interaction, robotics and knowledge mining.
Face Recognition with Hybrid Efficient Convolution Algorithms on FPGAs
Deep Convolutional Neural Networks have become a Swiss knife in solving critical artificial intelligence tasks.
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks.
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks
To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.
