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Found 27 papers, 12 papers with code
Speech Intelligibility Classifiers from 550k Disordered Speech Samples
We developed dysarthric speech intelligibility classifiers on 551, 176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking disorders and rated for their overall intelligibility on a five-point scale.
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings
Automatic Speech Recognition (ASR) in medical contexts has the potential to save time, cut costs, increase report accuracy, and reduce physician burnout.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Sparse Winning Tickets are Data-Efficient Image Recognizers
Improving the performance of deep networks in data-limited regimes has warranted much attention.
Assessing ASR Model Quality on Disordered Speech using BERTScore
BERTScore was found to be more correlated with human assessment of error type and assessment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Is Attention All That NeRF Needs?
While prior works on NeRFs optimize a scene representation by inverting a handcrafted rendering equation, GNT achieves neural representation and rendering that generalizes across scenes using transformers at two stages.
Context-Aware Abbreviation Expansion Using Large Language Models
Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters.
TRILLsson: Distilled Universal Paralinguistic Speech Representations
Our largest distilled model is less than 15% the size of the original model (314MB vs 2. 2GB), achieves over 96% the accuracy on 6 of 7 tasks, and is trained on 6. 5% the data.
Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images
We find that some target tasks are easily predicted irrespective of the source task, and that some other target tasks are more accurately predicted from correlated source tasks than from embeddings trained on the same task.
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment.
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
To minimize the effect of this source of noise, we propose adapting the attribution path itself -- conditioning the path not just on the image but also on the model being explained.
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing DR screening.
Scientific Discovery by Generating Counterfactuals using Image Translation
Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent.
Scaling Symbolic Methods using Gradients for Neural Model Explanation
In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation.
Attribution in Scale and Space
Third, it eliminates the need for a 'baseline' parameter for Integrated Gradients [31] for perception tasks.
It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets
Confounding variables are a well known source of nuisance in biomedical studies.
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME.
Detecting Cancer Metastases on Gigapixel Pathology Images
At 8 false positives per image, we detect 92. 4% of the tumors, relative to 82. 7% by the previous best automated approach.
Ranked #2 on
Medical Object Detection
on Barrett’s Esophagus
Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions
Image segmentation from referring expressions is a joint vision and language modeling task, where the input is an image and a textual expression describing a particular region in the image; and the goal is to localize and segment the specific image region based on the given expression.
Captioning Images with Diverse Objects
We propose minimizing a joint objective which can learn from these diverse data sources and leverage distributional semantic embeddings, enabling the model to generalize and describe novel objects outside of image-caption datasets.
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text
This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos.
Sequence to Sequence - Video to Text
Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip.
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Current deep caption models can only describe objects contained in paired image-sentence corpora, despite the fact that they are pre-trained with large object recognition datasets, namely ImageNet.
A Multi-scale Multiple Instance Video Description Network
Most state-of-the-art methods for solving this problem borrow existing deep convolutional neural network (CNN) architectures (AlexNet, GoogLeNet) to extract a visual representation of the input video.
Sequence to Sequence -- Video to Text
Our LSTM model is trained on video-sentence pairs and learns to associate a sequence of video frames to a sequence of words in order to generate a description of the event in the video clip.
Translating Videos to Natural Language Using Deep Recurrent Neural Networks
Solving the visual symbol grounding problem has long been a goal of artificial intelligence.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise.
Ranked #3 on
Human Interaction Recognition
on BIT
