Search Results for author:
Found 47 papers, 9 papers with code
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
DeepGate: Learning Neural Representations of Logic Gates
We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate.
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
Furthermore, we present CSS, a large-scale CrosS-Schema Chinese text-to-SQL dataset, to carry on corresponding studies.
DeepTPI: Test Point Insertion with Deep Reinforcement Learning
Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement.
Evolutionary Architecture Search for Graph Neural Networks
In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single Word
We demonstrate the challenges and effectiveness of modeling industrial big data by a Soft Sensing Transformer model on these data sets.
Testability-Aware Low Power Controller Design with Evolutionary Learning
The proposed GA-guided XORNets also allows reducing the number of control bits, and the total testing time decreases by 20. 78% on average and up to 47. 09% compared to the existing design without sacrificing test coverage.
Segmentation of MRI head anatomy using deep volumetric networks and multiple spatial priors
The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet.
The State and Future of Genetic Improvement
We report the discussion session at the sixth international Genetic Improvement workshop, GI-2019 @ ICSE, which was held as part of the 41st ACM/IEEE International Conference on Software Engineering on Tuesday 28th May 2019.
Attributed Rhetorical Structure Grammar for Domain Text Summarization
We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine.
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
Genetic Improvement @ ICSE 2020
Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020).
Physics-informed Tensor-train ConvLSTM for Volumetric Velocity Forecasting of Loop Current
According to the National Academies, a weekly forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies is critical for understanding the oceanography and ecosystem, and for mitigating outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM).
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans
Conclusion: When trained on a sufficiently large dataset, the developed 3D U-Net performed as well as fellowship-trained radiologists in detailed 2D segmentation of breast cancers at routine clinical MRI.
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis
To address the challenge, we propose a non-RL Bipartite Scalable framework for Online Disease diAgnosis, called BSODA.
Physical rendering of synthetic spaces for topological sound transport
Synthetic dimensions can be rendered in the physical space and this has been achieved with photonics and cold atomic gases, however, little to no work has been succeeded in acoustics because acoustic wave-guides cannot be weakly coupled in a continuous fashion.
Mesoscale and Nanoscale Physics Classical Physics
Regions of Attraction Estimation using Level SetMethod for Complex Network System
This approach is insufficient for dynamic networks with changing equilibrium and estimating the Region of Attraction(ROA) is needed.
Social and Information Networks
What Makes Multi-modal Learning Better than Single (Provably)
The world provides us with data of multiple modalities.
Snippet Policy Network for Multi-class Varied-length ECG Early Classification
To the best of our knowledge, this is the first work focusing on solving the cardiovascular early classification problem based on varied-length ECG data.
Physics-Coupled Spatio-Temporal Active Learning for Dynamical Systems
To tackle these challenges, we advocate a spatio-temporal physics-coupled neural networks (ST-PCNN) model to learn the underlying physics of the dynamical system and further couple the learned physics to assist the learning of the recurring dynamics.
ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting
A spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system.
IEEE BigData 2021 Cup: Soft Sensing at Scale
The scope of this challenge is to tackle the task of classifying soft sensing data with machine learning techniques.
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned
To understand how the proposed model works, the deep visualization approach is applied.
GraSSNet: Graph Soft Sensing Neural Networks
In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection.
Soft-Sensing ConFormer: A Curriculum Learning-based Convolutional Transformer
Over the last few decades, modern industrial processes have investigated several cost-effective methodologies to improve the productivity and yield of semiconductor manufacturing.
A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years.
Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)
Recently, it has been observed that the best uni-modal network outperforms the jointly trained multi-modal network, which is counter-intuitive since multiple signals generally bring more information.
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Precise congestion prediction from a placement solution plays a crucial role in circuit placement.
A Deep-Learning Usability Expansion Model of Ocean Observations
The caveat of this approach is that measurements are used only once, at the time of the prediction.
Provable Generalization of Overparameterized Meta-learning Trained with SGD
Despite the superior empirical success of deep meta-learning, theoretical understanding of overparameterized meta-learning is still limited.
Low Complexity First: Duration-Centric ISI Mitigation in Molecular Communication via Diffusion
In this paper, we propose a novel inter-symbol interference (ISI) mitigation scheme for molecular communication via diffusion (MCvD) systems with the optimal detection interval.
Pre-Training Representations of Binary Code Using Contrastive Learning
To the best of our knowledge, COMBO is the first language representation model that incorporates source code, binary code, and comments into contrastive code representation learning and unifies multiple tasks for binary code analysis.
Soft Sensing Regression Model: from Sensor to Wafer Metrology Forecasting
The semiconductor industry is one of the most technology-evolving and capital-intensive market sectors.
Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG
(2) Conflict-driven implication and justification have been applied to increase decision accuracy and solving efficiency.
An Overview about Emerging Technologies of Autonomous Driving
Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)
Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications.
In-Context Convergence of Transformers
For data with balanced features, we establish the finite-time convergence guarantee with near-zero prediction error by navigating our analysis over two phases of the training dynamics of the attention map.
What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?
With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention.
Applications of Large Scale Foundation Models for Autonomous Driving
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications.
Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection
Reevaluating these regions in 10% of all cases with higher AI-predicted risk could have resulted in up to 33% early detections by a radiologist.
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.
EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets.
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
Furthermore, alignment between model and human foci in this setting does not seem to dictate the quality of the LLM-generated summaries.
Feasibility of Identifying Factors Related to Alzheimer's Disease and Related Dementia in Real-World Data
We constructed an interactive knowledge map to disseminate our study results.
Transformers Provably Learn Feature-Position Correlations in Masked Image Modeling
Masked image modeling (MIM), which predicts randomly masked patches from unmasked ones, has emerged as a promising approach in self-supervised vision pretraining.
Accelerating Convergence of Score-Based Diffusion Models, Provably
Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampling phase.
HDLdebugger: Streamlining HDL debugging with Large Language Models
In the domain of chip design, Hardware Description Languages (HDLs) play a pivotal role.
