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Found 11 papers, 5 papers with code
Speech Recognition and Multi-Speaker Diarization of Long Conversations
Speech recognition (ASR) and speaker diarization (SD) models have traditionally been trained separately to produce rich conversation transcripts with speaker labels.
Deflecting Adversarial Attacks
There has been an ongoing cycle where stronger defenses against adversarial attacks are subsequently broken by a more advanced defense-aware attack.
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Then, we diagnose the adversarial examples for CapsNets and find that the success of the reconstructive attack is highly related to the visual similarity between the source and target class.
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Adversarial examples are inputs to machine learning models designed by an adversary to cause an incorrect output.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Ranked #5 on
Brain Tumor Segmentation
on BRATS-2015
Learning to see people like people
Humans make complex inferences on faces, ranging from objective properties (gender, ethnicity, expression, age, identity, etc) to subjective judgments (facial attractiveness, trustworthiness, sociability, friendliness, etc).
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.
Understanding Convolution for Semantic Segmentation
This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.
Ranked #20 on
Semantic Segmentation
on PASCAL VOC 2012 test
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration
Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows.
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition
Our results suggest that the relative order of importance of using central visual field information is face recognition>object recognition>scene recognition, and vice-versa for peripheral information.
Efficient Visual Coding: From Retina To V2
The human visual system has a hierarchical structure consisting of layers of processing, such as the retina, V1, V2, etc.
