Search Results for author:
Found 59 papers, 18 papers with code
Parametric Contrastive Learning
In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition.
Ranked #12 on
Long-tail Learning
on iNaturalist 2018
Generalized Parametric Contrastive Learning
Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning.
Ranked #5 on
Long-tail Learning
on iNaturalist 2018
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness
Second, with the proposed concept of Model Prediction Bias, we investigate the origins of problematic representation during optimization.
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
Instruction tuning, a successful paradigm, enhances the ability of LLMs to follow natural language instructions and exhibit robust generalization across a wide range of tasks.
Ranked #4 on
Common Sense Reasoning
on ARC (Easy)
DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors
First, to effectively lift the 2D information to stereo volume, we propose depth-wise plane sweeping (DPS) that allows denser connections and extracts depth-guided features.
PCL: Proxy-Based Contrastive Learning for Domain Generalization
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #17 on
Domain Generalization
on PACS
Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment
Low-light video enhancement is an important task.
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing.
Decoupled Kullback-Leibler Divergence Loss
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and observe that it is equivalent to the Doupled Kullback-Leibler (DKL) Divergence loss that consists of 1) a weighted Mean Square Error (wMSE) loss and 2) a Cross-Entropy loss incorporating soft labels.
Automatic Layout Generation with Applications in Machine Learning Engine Evaluation
The tool currently supports both metal layer and via layer generation.
Measuring Correlation-to-Causation Exaggeration in Press Releases
By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22{\%} of press releases made exaggerated causal claims from correlational findings in observational studies.
p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models
In this paper, we present a novel modeling framework that recasts adapter tuning after attention as a graph message passing process on attention graphs, where the projected query and value features and attention matrix constitute the node features and the graph adjacency matrix, respectively.
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach
In spite of maturity to the modern electronic design automation (EDA) tools, optimized designs at architectural stage may become sub-optimal after going through physical design flow.
PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask
In this work, we revisit the prior mask guidance proposed in ``Prior Guided Feature Enrichment Network for Few-Shot Segmentation''.
Detecting Health Advice in Medical Research Literature
We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures.
Self Promotion in US Congressional Tweets
Prior studies have found that women self-promote less than men due to gender stereotypes.
Bridging the Gap Between Layout Pattern Sampling and Hotspot Detection via Batch Active Sampling
Layout hotpot detection is one of the main steps in modern VLSI design.
Recent Advances in Convolutional Neural Network Acceleration
We propose a taxonomy in terms of three levels, i. e.~structure level, algorithm level, and implementation level, for acceleration methods.
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference.
DAC-SDC Low Power Object Detection Challenge for UAV Applications
The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018.
An Evaluation of Information Extraction Tools for Identifying Health Claims in News Headlines
This study evaluates the performance of four information extraction tools (extractors) on identifying health claims in health news headlines.
An NLP Analysis of Exaggerated Claims in Science News
The discrepancy between science and media has been affecting the effectiveness of science communication.
DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems
Furthermore, DeepBillboard is sufficiently robust and resilient for generating physical-world adversarial billboard tests for real-world driving under various weather conditions.
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection
There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning.
Detecting Causal Language Use in Science Findings
We then applied the prediction model to measure the causal language use in the research conclusions of about 38, 000 observational studies in PubMed.
VLSI Mask Optimization: From Shallow To Deep Learning
VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation.
Attacking Split Manufacturing from a Deep Learning Perspective
The notion of integrated circuit split manufacturing which delegates the front-end-of-line (FEOL) and back-end-of-line (BEOL) parts to different foundries, is to prevent overproduction, piracy of the intellectual property (IP), or targeted insertion of hardware Trojans by adversaries in the FEOL facility.
Dive Deeper Into Box for Object Detection
Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods.
Tensor Low-Rank Reconstruction for Semantic Segmentation
Then we use these rank-1 tensors to recover the high-rank context features through our proposed tensor reconstruction module (TRM).
Rethinking Graph Neural Networks for Graph Coloring
To characterize the power of GNNs for the graph coloring problem, we first formalize the discrimination power of GNNs as the capability to assign nodes different colors.
Routing Towards Discriminative Power of Class Capsules
The activation of lower layer capsules affects the behavior of the following capsules via routing links that are constructed during training via certain routing algorithms.
Linking Health News to Research Literature
Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news.
Conditional Temporal Variational AutoEncoder for Action Video Prediction
To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video.
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning
Firstly, a deep Gaussian process (DGP) model is built on the historical data to learn empirical knowledge.
Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform
In this paper, Eventor is proposed as a fast and efficient EMVS accelerator by realizing the most critical and time-consuming stages including event back-projection and volumetric ray-counting on FPGA.
Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network
Moreover, we also propose a new video denoising framework, called Recurrent Video Denoising Transformer (RVDT), which can achieve SOTA performance on PVDD and other current video denoising benchmarks.
“Devils Are in the Details”: Annotating Specificity of Clinical Advice from Medical Literature
Prior studies have raised concerns over specificity issues in clinical advice.
Rethinking Graph Neural Networks for the Graph Coloring Problem
Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible.
Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields
Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead.
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU
Recent years have witnessed impressive progress in super-resolution (SR) processing.
DevelSet: Deep Neural Level Set for Instant Mask Optimization
To further enhance printability and fast iterative convergence, we propose a novel deep neural network delicately designed with level set intrinsic principles to facilitate the joint optimization of DNN and GPU accelerated level set optimizer.
AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns
We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity.
DiffPattern: Layout Pattern Generation via Discrete Diffusion
Deep generative models dominate the existing literature in layout pattern generation.
GPU-accelerated Matrix Cover Algorithm for Multiple Patterning Layout Decomposition
Multiple patterning lithography (MPL) is regarded as one of the most promising ways of overcoming the resolution limitations of conventional optical lithography due to the delay of next-generation lithography technology.
Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters
To inject visual knowledge into PLMs, existing methods incorporate either the text or image encoder of vision-language models (VLMs) to encode the visual information and update all the original parameters of PLMs for knowledge fusion.
ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
The integration of a complex set of Electronic Design Automation (EDA) tools to enhance interoperability is a critical concern for circuit designers.
Efficient Deep Space Filling Curve
By modeling the pixel grid as a graph, they first adopt GNN to predict the edge weights and then generate a minimum spanning tree (MST) based on the predictions, which is further used to construct the SFC.
On the Evaluation of Generative Models in Distributed Learning Tasks
First, we focus on the Fr\'echet inception distance (FID) and consider the following FID-based aggregate scores over the clients: 1) FID-avg as the mean of clients' individual FID scores, 2) FID-all as the FID distance of the trained model to the collective dataset containing all clients' data.
TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices
To the best of our knowledge, SparseEngine is the first deployment framework capable of performing inference of sparse models with transformer on MCUs.
BetterV: Controlled Verilog Generation with Discriminative Guidance
In this work, we propose a Verilog generation framework, BetterV, which fine-tunes the large language models (LLMs) on processed domain-specific datasets and incorporates generative discriminators for guidance on particular design demands.
SoLA: Solver-Layer Adaption of LLM for Better Logic Reasoning
In this paper, we propose a novel solver-layer adaptation (SoLA) method, where we introduce a solver as a new layer of the LLM to differentially guide solutions towards satisfiability.
Learning-driven Physically-aware Large-scale Circuit Gate Sizing
Gate sizing plays an important role in timing optimization after physical design.
HDLdebugger: Streamlining HDL debugging with Large Language Models
In the domain of chip design, Hardware Description Languages (HDLs) play a pivotal role.
ChatPattern: Layout Pattern Customization via Natural Language
Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention.
CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
Optical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing.
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
Graph Neural Networks (GNNs) succeed significantly in many applications recently.
