Search Results for author:
Found 15 papers, 3 papers with code
Energy-efficiency Limits on Training AI Systems using Learning-in-Memory
In this paper, we derive new theoretical lower bounds on energy dissipation when training AI systems using different LIM approaches.
Statistical Barriers to Affine-equivariant Estimation
We investigate the quantitative performance of affine-equivariant estimators for robust mean estimation.
Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer
Cardiac Magnetic Resonance imaging (CMR) is the gold standard for assessing cardiac function.
Energy landscape reveals the underlying mechanism of cancer-adipose conversion with gene network models
Our results revealed the underlying mechanism for intermediate cell states governing the CAC, and identified new potential drug combinations to induce cancer adipogenesis.
An Interpretable Loan Credit Evaluation Method Based on Rule Representation Learner
More importantly, our model is used to test the correctness of the explanations generated by the post-hoc method, the results show that the post-hoc method is not always reliable.
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applications
To overcome this, we propose a novel Japanese sentence representation framework, JCSE (derived from ``Contrastive learning of Sentence Embeddings for Japanese''), that creates training data by generating sentences and synthesizing them with sentences available in a target domain.
Break The Spell Of Total Correlation In betaTCVAE
The novel model enables VAE to adjust the parameter capacity to divide dependent and independent data features flexibly.
Magnitude-image based data-consistent deep learning method for MRI super resolution
Magnetic Resonance Imaging (MRI) is important in clinic to produce high resolution images for diagnosis, but its acquisition time is long for high resolution images.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction
Non-Cartesian sampling with subspace-constrained image reconstruction is a popular approach to dynamic MRI, but slow iterative reconstruction limits its clinical application.
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
In spite of its remarkable progress, many algorithms are restricted to particular search spaces.
Ranked #13 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
Accelerating Training using Tensor Decomposition
In our approach, we train the model from scratch (i. e., randomly initialized weights) with its original architecture for a small number of epochs, then the model is decomposed, and then continue training the decomposed model till the end.
DeepShift: Towards Multiplication-Less Neural Networks
This family of neural network architectures (that use convolutional shifts and fully connected shifts) is referred to as DeepShift models.
Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features
Recently, there has been an increasing interest in designing distributed convex optimization algorithms under the setting where the data matrix is partitioned on features.
A Proximal Stochastic Quasi-Newton Algorithm
It incorporates the Hessian in the smooth part of the function and exploits multistage scheme to reduce the variance of the stochastic gradient.
