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Found 22 papers, 2 papers with code
Using External Off-Policy Speech-To-Text Mappings in Contextual End-To-End Automated Speech Recognition
Despite improvements to the generalization performance of automated speech recognition (ASR) models, specializing ASR models for downstream tasks remains a challenging task, primarily due to reduced data availability (necessitating increased data collection), and rapidly shifting data distributions (requiring more frequent model fine-tuning).
Disentangled Action Recognition with Knowledge Bases
In this paper, we aim to improve the generalization ability of the compositional action recognition model to novel verbs or novel nouns that are unseen during training time, by leveraging the power of knowledge graphs.
Content-Context Factorized Representations for Automated Speech Recognition
Deep neural networks have largely demonstrated their ability to perform automated speech recognition (ASR) by extracting meaningful features from input audio frames.
Unified Modeling of Multi-Domain Multi-Device ASR Systems
Modern Automatic Speech Recognition (ASR) systems often use a portfolio of domain-specific models in order to get high accuracy for distinct user utterance types across different devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Multi-Modal Pre-Training for Automated Speech Recognition
Traditionally, research in automated speech recognition has focused on local-first encoding of audio representations to predict the spoken phonemes in an utterance.
Hierarchical Class-Based Curriculum Loss
We theoretically show that the proposed loss function is a tighter bound of 0-1 loss compared to any other loss satisfying the hierarchical constraints.
Cross-modal Learning for Multi-modal Video Categorization
Multi-modal machine learning (ML) models can process data in multiple modalities (e. g., video, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding, activity recognition).
Exploiting Temporal Coherence for Multi-modal Video Categorization
Multimodal ML models can process data in multiple modalities (e. g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding).
RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge
The key component of RILOD is a novel incremental learning algorithm that trains end-to-end for one-stage deep object detection models only using training data of new object classes.
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML
Lifelong learning, the problem of continual learning where tasks arrive in sequence, has been lately attracting more attention in the computer vision community.
Class-incremental Learning via Deep Model Consolidation
The idea is to first train a separate model only for the new classes, and then combine the two individual models trained on data of two distinct set of classes (old classes and new classes) via a novel double distillation training objective.
Generative Visual Dialogue System via Adaptive Reasoning and Weighted Likelihood Estimation
The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow.
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention
In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i. e., generating natural language (NL) explanations for the Visual Question Answering (VQA) problem.
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded
Many vision and language models suffer from poor visual grounding - often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the image.
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression
State-of-the-art deep model compression methods exploit the low-rank approximation and sparsity pruning to remove redundant parameters from a learned hidden layer.
Data Masking with Privacy Guarantees
In particular, we propose a method of masking the private data with privacy guarantee while ensuring that a classifier trained on the masked data is similar to the classifier trained on the original data, to maintain usability.
Time Series Deinterleaving of DNS Traffic
Stream deinterleaving is an important problem with various applications in the cybersecurity domain.
Trusted Neural Networks for Safety-Constrained Autonomous Control
Experiments using our first approach of a multi-headed TNN model, on a dataset generated by a customized version of TORCS, show that (1) adding safety constraints to a neural network model results in increased performance and safety, and (2) the improvement increases with increasing importance of the safety constraints.
A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target domain can be integrated by exploiting the curved Riemannian geometry of statistical manifolds.
Contextual LSTM (CLSTM) models for Large scale NLP tasks
We evaluate CLSTM on three specific NLP tasks: word prediction, next sentence selection, and sentence topic prediction.
Virus Detection in Multiplexed Nanowire Arrays using Hidden Semi-Markov models
In this paper, we address the problem of real-time detection of viruses docking to nanowires, especially when multiple viruses dock to the same nano-wire.
ARSENAL: Automatic Requirements Specification Extraction from Natural Language
Requirements are informal and semi-formal descriptions of the expected behavior of a complex system from the viewpoints of its stakeholders (customers, users, operators, designers, and engineers).
