Search Results for author:
Found 40 papers, 15 papers with code
Splitting vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation
Finally, by training the segmentation model with the masks generated by our Splitting vs Merging strategy, we achieve the state-of-the-art weakly-supervised segmentation results on the Pascal VOC 2012 benchmark.
Weakly supervised segmentation
Weakly-Supervised Semantic Segmentation
Pancreatic Cancer ROSE Image Classification Based on Multiple Instance Learning with Shuffle Instances
Computer-aided diagnosis (CAD) using the deep learning method has the potential to solve the problem of insufficient pathology staffing.
Decentralized Training of Foundation Models in Heterogeneous Environments
Our key technical contribution is a scheduling algorithm that allocates different computational "tasklets" in the training of foundation models to a group of decentralized GPU devices connected by a slow heterogeneous network.
TempLM: Distilling Language Models into Template-Based Generators
While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content.
Model-Based Neural Network and Its Application to Line Spectral Estimation
Owing to the same layered form as an ANN, a MNN can also be optimized using the back-propagation (BP) algorithm.
MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer
Pancreatic cancer is one of the most malignant cancers in the world, which deteriorates rapidly with very high mortality.
Rethinking Importance Weighting for Transfer Learning
A key assumption in supervised learning is that training and test data follow the same probability distribution.
PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records
Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communication technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays.
Learning Cross-Scale Visual Representations for Real-Time Image Geo-Localization
Robot localization remains a challenging task in GPS denied environments.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
In this paper we provide, to the best of our knowledge, the first comprehensive approach for incorporating various masking mechanisms into Transformers architectures in a scalable way.
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery
Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines.
On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies
We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains.
Sinusoidal Parameter Estimation from Signed Measurements via Majorization-Minimization Based RELAX
In a previous paper, a relaxation-based algorithm, referred to as 1bRELAX, has been proposed to iteratively maximize the likelihood function.
Joint RFI Mitigation and Radar Echo Recovery for One-Bit UWB Radar
Radio frequency interference (RFI) mitigation and radar echo recovery are critically important for the proper functioning of ultra-wideband (UWB) radar systems using one-bit sampling techniques.
Learning to Stop with Surprisingly Few Samples
We consider a discounted infinite horizon optimal stopping problem.
RFI Mitigation for One-bit UWB Radar Systems
A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the Continuous Time Binary Value (CTBV) technology.
Second- and third-order optical susceptibilities in bidimensional semiconductors near excitons states
Semiconducting Transition Metal Dichalcogenides (TMDs) have significant nonlinear optical effects.
Mesoscale and Nanoscale Physics
Generative Adversarial Network based Heuristics for Sampling-based Path Planning
Sampling-based path planning is a popular methodology for robot path planning.
Can Steering Wheel Detect Your Driving Fatigue?
In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel.
ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses
Existing solution recommendation methods for online customer service are unable to determine the best solutions at runtime, leading to poor satisfaction of end customers.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.
A One-step Approach to Covariate Shift Adaptation
A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution.
Revisiting Few-sample BERT Fine-tuning
We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process.
Demystifying Orthogonal Monte Carlo and Beyond
Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction.
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules.
Supporting OpenMP 5.0 Tasks in hpxMP -- A study of an OpenMP implementation within Task Based Runtime Systems
This paper is a follow-up paper on the fundamental implementation of hpxMP, an implementation of the OpenMP standard which utilizes the C++ standard library for Parallelism and Concurrency (HPX) to schedule and manage tasks.
Distributed, Parallel, and Cluster Computing Programming Languages
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a blackbox attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e. g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.
Identifying Mislabeled Data using the Area Under the Margin Ranking
Not all data in a typical training set help with generalization; some samples can be overly ambiguous or outrightly mislabeled.
Scalable NAS with Factorizable Architectural Parameters
Neural Architecture Search (NAS) is an emerging topic in machine learning and computer vision.
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
The recently proposed unlabeled-unlabeled (UU) classification method allows us to train a binary classifier only from two unlabeled datasets with different class priors.
QPyTorch: A Low-Precision Arithmetic Simulation Framework
Low-precision training reduces computational cost and produces efficient models.
Detecting Noisy Training Data with Loss Curves
This paper introduces a new method to discover mislabeled training samples and to mitigate their impact on the training process of deep networks.
Fixed-price Diffusion Mechanism Design
We consider a fixed-price mechanism design setting where a seller sells one item via a social network, but the seller can only directly communicate with her neighbours initially.
SWALP : Stochastic Weight Averaging in Low-Precision Training
Low precision operations can provide scalability, memory savings, portability, and energy efficiency.
BERTScore: Evaluating Text Generation with BERT
We propose BERTScore, an automatic evaluation metric for text generation.
An Introduction to hpxMP: A Modern OpenMP Implementation Leveraging HPX, An Asynchronous Many-Task System
Not only must users port their own codes, but often users rely on highly optimized libraries such as BLAS and LAPACK which use OpenMP for parallization.
Distributed, Parallel, and Cluster Computing
Simplifying Graph Convolutional Networks
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
Ranked #3 on
Text Classification
on 20NEWS
(using extra training data)
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference
In this paper, we apply the variance reduction tricks on Hamiltonian Monte Carlo and achieve better theoretical convergence results compared with the variance-reduced Langevin dynamics.
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation
In our work, we focus on the weakly supervised semantic segmentation with image label annotations.
