Search Results for author:
Found 16 papers, 4 papers with code
Federated Representation Learning for Automatic Speech Recognition
Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data.
Federated Self-Learning with Weak Supervision for Speech Recognition
Automatic speech recognition (ASR) models with low-footprint are increasingly being deployed on edge devices for conversational agents, which enhances privacy.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Learning When to Trust Which Teacher for Weakly Supervised ASR
We show the efficacy of our approach using LibriSpeech and LibriLight benchmarks and find an improvement of 4 to 25\% over baselines that uniformly weight all the experts, use a single expert model, or combine experts using ROVER.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Can Calibration Improve Sample Prioritization?
Calibration can reduce overconfident predictions of deep neural networks, but can calibration also accelerate training?
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale
For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques
We extrapolate the basic block execution counts of GPU applications and use them for predicting the performance for large input sizes from the counts of smaller input sizes.
An Effective Baseline for Robustness to Distributional Shift
In this work we present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention: when encountering a sample from an unseen class, the desired behavior is to abstain from predicting.
Ranked #1 on
Out-of-Distribution Detection
on CIFAR-100
(using extra training data)
A Simple and Effective Baseline for Out-of-Distribution Detection using Abstention
In this work we present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention: when encountering a sample from an unseen class, the desired behavior is to abstain from predicting.
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking
Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity.
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest.
Distributed Non-Negative Tensor Train Decomposition
Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors.
Combating Label Noise in Deep Learning Using Abstention
In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise.
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
In this work, we discuss a hitherto untouched aspect of mixup training -- the calibration and predictive uncertainty of models trained with mixup.
Ranked #1 on
Out-of-Distribution Detection
on STL-10
Knows When it Doesn’t Know: Deep Abstaining Classifiers
We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training.
Quantum Algorithm Implementations for Beginners
As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers.
Emerging Technologies Quantum Physics
eAnt-Miner : An Ensemble Ant-Miner to Improve the ACO Classification
In this paper, to address this issue, an acclaimed machine learning technique named, ensemble of classifiers is applied, where an ACO classifier is used as a base classifier to prepare the ensemble.
