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Found 16 papers, 6 papers with code
Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes
Dice scores of both DL models were sufficiently high (>0. 85), and their AUCs for disease classification were superior to that of FS.
AI-based computer-aided diagnostic system of chest digital tomography synthesis: Demonstrating comparative advantage with X-ray-based AI systems
The proposed CDTS-based AI CAD system yielded sensitivities of 0. 782 and 0. 785 and accuracies of 0. 895 and 0. 837 for the performance of detecting tuberculosis and pneumonia, respectively, against normal subjects.
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography
The proposed method based on unsupervised learning improves the patient-level anomaly detection by 10% (area under the curve, 0. 959) compared with a gold standard based on supervised learning (area under the curve, 0. 848), and it localizes the anomaly region with 93% accuracy, demonstrating its high performance.
Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks
In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45%.
Discovery of an insulating ferromagnetic phase of electrons in two dimensions
This is a commentary on two papers (PNAS 117 (51) 32244-32250 (2020) and arXiv:2011. 06721), which observed a series of ordering transitions in a strongly correlated two-dimensional electron system confined to a AlAs quantum well.
Strongly Correlated Electrons Mesoscale and Nanoscale Physics Materials Science
Modality Shifting Attention Network for Multi-modal Video Question Answering
To this end, MSAN is based on (1) the moment proposal network (MPN) that attempts to locate the most appropriate temporal moment from each of the modalities, and also on (2) the heterogeneous reasoning network (HRN) that predicts the answer using an attention mechanism on both modalities.
Compressed Sensing via Measurement-Conditional Generative Models
A pre-trained generator has been frequently adopted in compressed sensing (CS) due to its ability to effectively estimate signals with the prior of NNs.
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
(2) A generalization error bound invariant of network size was derived by using a data-dependent complexity measure (CMD).
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
For the gradient computation across the time domain in Spiking Neural Networks (SNNs) training, two different approaches have been independently studied.
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations
Binary Neural Networks (BNNs) have been garnering interest thanks to their compute cost reduction and memory savings.
Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling
In this paper, we propose to explicitly learn to imagine a storyline that bridges the visual gap.
Ranked #7 on
Visual Storytelling
on VIST
Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering
This paper proposes a method to gain extra supervision via multi-task learning for multi-modal video question answering.
Progressive Attention Memory Network for Movie Story Question Answering
To overcome these challenges, PAMN involves three main features: (1) progressive attention mechanism that utilizes cues from both question and answer to progressively prune out irrelevant temporal parts in memory, (2) dynamic modality fusion that adaptively determines the contribution of each modality for answering the current question, and (3) belief correction answering scheme that successively corrects the prediction score on each candidate answer.
Ranked #2 on
Video Story QA
on MovieQA
Fourier Phase Retrieval with Extended Support Estimation via Deep Neural Network
We consider the problem of sparse phase retrieval from Fourier transform magnitudes to recover the $k$-sparse signal vector and its support $\mathcal{T}$.
Tree Search Network for Sparse Regression
We consider the classical sparse regression problem of recovering a sparse signal $x_0$ given a measurement vector $y = \Phi x_0+w$.
Learning Not to Learn: Training Deep Neural Networks with Biased Data
We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased.
