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Found 67 papers, 23 papers with code
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces
Bayesian optimization (BO) is a popular black-box function optimization method, which makes sequential decisions based on a Bayesian model, typically a Gaussian process (GP), of the function.
A plug-and-play synthetic data deep learning for undersampled magnetic resonance image reconstruction
Magnetic resonance imaging (MRI) plays an important role in modern medical diagnostic but suffers from prolonged scan time.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time.
HeroLT: Benchmarking Heterogeneous Long-Tailed Learning
To achieve this, we develop the most comprehensive (to the best of our knowledge) long-tailed learning benchmark named HeroLT, which integrates 13 state-of-the-art algorithms and 6 evaluation metrics on 14 real-world benchmark datasets across 4 tasks from 3 domains.
On Evaluating Multilingual Compositional Generalization with Translated Datasets
To address this limitation, we craft a faithful rule-based translation of the MCWQ dataset from English to Chinese and Japanese.
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples.
Gaussian Process Probes (GPP) for Uncertainty-Aware Probing
Our experiments show it can (1) probe a model's representations of concepts even with a very small number of examples, (2) accurately measure both epistemic uncertainty (how confident the probe is) and aleatory uncertainty (how fuzzy the concepts are to the model), and (3) detect out of distribution data using those uncertainty measures as well as classic methods do.
Dynamic Enhancement Network for Partial Multi-modality Person Re-identification
Many existing multi-modality studies are based on the assumption of modality integrity.
Rethink Diversity in Deep Learning Testing
Deep neural networks (DNNs) have demonstrated extraordinary capabilities and are an integral part of modern software systems.
Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification
Finally, to evaluate the proposed FACENet in handling intense flare, we introduce a new multi-spectral vehicle re-ID dataset, called WMVEID863, with additional challenges such as motion blur, significant background changes, and particularly intense flare degradation.
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving
To achieve comprehensive recovery, we design a communication adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information.
Efficient Symbolic Reasoning for Neural-Network Verification
They allow us to encode many verification problems for neural networks as quadratic programs.
TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Driving
Second, a spatial-temporal pyramid transformer is introduced to comprehensively extract multi-scale BEV features and predict future BEV states with the support of spatial-temporal priors.
Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey
Oriented object detection is one of the most fundamental and challenging tasks in remote sensing, aiming at locating the oriented objects of numerous predefined object categories.
Enhancing Non-line-of-sight Imaging via Learnable Inverse Kernel and Attention Mechanisms
Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem.
Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraints
Recently, unsupervised domain adaptation in satellite pose estimation has gained increasing attention, aiming at alleviating the annotation cost for training deep models.
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes
However, those prior learning methods typically assume that the input domains are the same for all tasks, weakening their ability to use observations on functions with different domains or generalize the learned priors to BO on different search spaces.
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts.
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results.
Physics-informed deep diffusion MRI reconstruction: break the bottleneck of training data in artificial intelligence
In this work, we propose a Physics-Informed Deep Diffusion magnetic resonance imaging (DWI) reconstruction method (PIDD).
Parallel Augmentation and Dual Enhancement for Occluded Person Re-identification
To alleviate the above problems, we propose a simple but effective method with Parallel Augmentation and Dual Enhancement (PADE) that is robust on both occluded and non-occluded data, and does not require any auxiliary clues.
Plex: Towards Reliability using Pretrained Large Model Extensions
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures.
Pre-training helps Bayesian optimization too
Contrary to a common belief that BO is suited to optimizing black-box functions, it actually requires domain knowledge on characteristics of those functions to deploy BO successfully.
Towards Learning Universal Hyperparameter Optimizers with Transformers
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution.
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Deep learning has innovated the field of computational imaging.
DialogueNeRF: Towards Realistic Avatar Face-to-Face Conversation Video Generation
In this paper, we propose a novel unified framework based on neural radiance field (NeRF) to address this task.
A Quantitative Geometric Approach to Neural-Network Smoothness
In this work, we provide a unified theoretical framework, a quantitative geometric approach, to address the Lipschitz constant estimation.
Onsite Non-Line-of-Sight Imaging via Online Calibrations
There has been an increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects behind an obstacle.
One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI).
Pre-trained Gaussian processes for Bayesian optimization
Contrary to a common expectation that BO is suited to optimizing black-box functions, it actually requires domain knowledge about those functions to deploy BO successfully.
Zero-Shot Knowledge Distillation from a Decision-Based Black-Box Model
Here we propose the concept of decision-based black-box (DB3) knowledge distillation, with which the student is trained by distilling the knowledge from a black-box teacher (parameters are not accessible) that only returns classes rather than softmax outputs.
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis
Knowledge distillation (KD) has proved to be an effective approach for deep neural network compression, which learns a compact network (student) by transferring the knowledge from a pre-trained, over-parameterized network (teacher).
Convolutional Neural Network Pruning with Structural Redundancy Reduction
Based on this finding, we then propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy.
Engineering the light coupling between metalens and photonic crystal resonators for robust on-chip microsystems
We designed an on-chip transformative optic system of a metalens-photonic crystal resonator metasystem on a foundry compatible silicon photonic platform.
Optics
Exploring Adversarial Robustness of Deep Metric Learning
Deep Metric Learning (DML), a widely-used technique, involves learning a distance metric between pairs of samples.
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data
A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).
Non-line-of-Sight Imaging via Neural Transient Fields
We present a neural modeling framework for Non-Line-of-Sight (NLOS) imaging.
Generalized Universal Approximation for Certified Networks
To certify safety and robustness of neural networks, researchers have successfully applied abstract interpretation, primarily using interval bound propagation.
Adversarial Deep Metric Learning
To the best of our knowledge, we are the first to systematically analyze this dependence effect and propose a principled approach for robust training of deep metric learning networks that accounts for the nuances of metric losses.
A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.
Exponential Signal Reconstruction with Deep Hankel Matrix Factorization
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing.
Interval Universal Approximation for Neural Networks
This is a crucial question, as our constructive proof of IUA is exponential in the size of the approximation domain.
Learning compositional models of robot skills for task and motion planning
We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling.
TOFU: Target-Oriented FUzzer
TOFU is also input-structure aware (i. e., the search makes use of a specification of a superset of the program's allowed inputs).
Software Engineering
Semantic Robustness of Models of Source Code
Deep neural networks are vulnerable to adversarial examples - small input perturbations that result in incorrect predictions.
Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
Investigating Channel Pruning through Structural Redundancy Reduction -- A Statistical Study
Most existing channel pruning methods formulate the pruning task from a perspective of inefficiency reduction which iteratively rank and remove the least important filters, or find the set of filters that minimizes some reconstruction errors after pruning.
Single-shot Channel Pruning Based on Alternating Direction Method of Multipliers
In this work, we propose a novel single-shot channel pruning approach based on alternating direction methods of multipliers (ADMM), which can eliminate the need for complex iterative pruning and fine-tuning procedure and achieve a target compression ratio with only one run of pruning and fine-tuning.
Speeding up convolutional networks pruning with coarse ranking
In specific, with the proposed method, 75% and 54% of the total computation time for the whole pruning procedure can be reduced for AlexNet on CIFAR-10, and for VGG-16 on ImageNet, respectively.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Bayesian optimization usually assumes that a Bayesian prior is given.
Learning sparse relational transition models
For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state.
Learning to guide task and motion planning using score-space representation
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems.
Fast-converging Conditional Generative Adversarial Networks for Image Synthesis
Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by conditioning with certain additional information.
Active model learning and diverse action sampling for task and motion planning
Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world.
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis
However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulation networks at the right scales.
Batched Large-scale Bayesian Optimization in High-dimensional Spaces
Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries.
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Optimization of high-dimensional black-box functions is an extremely challenging problem.
Max-value Entropy Search for Efficient Bayesian Optimization
We propose a new criterion, Max-value Entropy Search (MES), that instead uses the information about the maximum function value.
Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models.
Studying the brain from adolescence to adulthood through sparse multi-view matrix factorisations
These latent factors can be used to produce low-dimensional visualisations of the data that emphasise age-specific effects once the shared effects have been accounted for.
Optimization as Estimation with Gaussian Processes in Bandit Settings
Recently, there has been rising interest in Bayesian optimization -- the optimization of an unknown function with assumptions usually expressed by a Gaussian Process (GP) prior.
Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons
The proposed methodology can be interpreted as an extension of principal component analysis in that it provides the means to decompose the total sample variance in each tissue into the sum of two components: one capturing the variance that is shared across tissues, and one isolating the tissue-specific variances.
An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization
We propose a distributed first-order augmented Lagrangian (DFAL) algorithm to minimize the sum of composite convex functions, where each term in the sum is a private cost function belonging to a node, and only nodes connected by an edge can directly communicate with each other.
Optimization and Control
Scalable Inference for Logistic-Normal Topic Models
Logistic-normal topic models can effectively discover correlation structures among latent topics.
