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Found 8 papers, 0 papers with code
Detecting a Proxy for Potential Comorbid ADHD in People Reporting Anxiety Symptoms from Social Media Data
We show how data that bears on ADHD that is comorbid with anxiety can be obtained from social media data, and show that Transformers can be used to detect a proxy for possible comorbid ADHD in people with anxiety symptoms.
Toward Learning Latent-Variable Representations of Microstructures by Optimizing in Spatial Statistics Space
In this work, we train a Variational Autoencoders (VAE) to produce reconstructions of textures that preserve the spatial statistics of the original texture, while not necessarily reconstructing the same image in data space.
Breaking Symmetry When Training Transformers
As we show in this paper, the prediction for output token $n+1$ of Transformer architectures without one of the mechanisms of positional encodings and causal attention is invariant to permutations of input tokens $1, 2, ..., n-1$.
Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency
We propose a reinforcement learning (RL)-based system that would automatically prescribe a hypothetical patient medication that may help the patient with their mental health-related speech disfluency, and adjust the medication and the dosages in response to zero-cost frequent measurement of the fluency of the patient.
How Do ConvNets Understand Image Intensity?
Convolutional Neural Networks (ConvNets) usually rely on edge/shape information to classify images.
Automatic Photo Orientation Detection with Convolutional Neural Networks
We apply convolutional neural networks (CNN) to the problem of image orientation detection in the context of determining the correct orientation (from 0, 90, 180, and 270 degrees) of a consumer photo.
Boosting Local Spectro-Temporal Features for Speech Analysis
We introduce the problem of phone classification in the context of speech recognition, and explore several sets of local spectro-temporal features that can be used for phone classification.
Salient Facial Features from Humans and Deep Neural Networks
In this work, we explore the features that are used by humans and by convolutional neural networks (ConvNets) to classify faces.
