16 research outputs found
Residual Alignment: Uncovering the Mechanisms of Residual Networks
The ResNet architecture has been widely adopted in deep learning due to its
significant boost to performance through the use of simple skip connections,
yet the underlying mechanisms leading to its success remain largely unknown. In
this paper, we conduct a thorough empirical study of the ResNet architecture in
classification tasks by linearizing its constituent residual blocks using
Residual Jacobians and measuring their singular value decompositions. Our
measurements reveal a process called Residual Alignment (RA) characterized by
four properties:
(RA1) intermediate representations of a given input are equispaced on a line,
embedded in high dimensional space, as observed by Gai and Zhang [2021];
(RA2) top left and right singular vectors of Residual Jacobians align with
each other and across different depths;
(RA3) Residual Jacobians are at most rank C for fully-connected ResNets,
where C is the number of classes; and
(RA4) top singular values of Residual Jacobians scale inversely with depth.
RA consistently occurs in models that generalize well, in both
fully-connected and convolutional architectures, across various depths and
widths, for varying numbers of classes, on all tested benchmark datasets, but
ceases to occur once the skip connections are removed. It also provably occurs
in a novel mathematical model we propose. This phenomenon reveals a strong
alignment between residual branches of a ResNet (RA2+4), imparting a highly
rigid geometric structure to the intermediate representations as they progress
linearly through the network (RA1) up to the final layer, where they undergo
Neural Collapse.Comment: Accepted at NeurIPS 2023 as a Poster pape
Combining Empirical and Physics-Based Models for Solar Wind Prediction
Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to enhance solar wind prediction. This innovative method aims to combine the strengths of both modeling approaches to achieve more accurate and efficient solar wind predictions. In this work, we show the variability of the proposed physics-informed loss across multiple deep learning models. We also study the effect of training the models on different solar cycles on the model\u27s performance. This work represents the first effort to predict solar wind by integrating deep learning approaches with physics constraints and analyzing the results across three solar cycles. Our findings demonstrate the superiority of our physics-constrained model over other unconstrained deep learning predictive models
A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media
Over the last decade, there has been a vast increase in eating disorder
diagnoses and eating disorder-attributed deaths, reaching their zenith during
the Covid-19 pandemic. This immense growth derived in part from the stressors
of the pandemic but also from increased exposure to social media, which is rife
with content that promotes eating disorders. Such content can induce eating
disorders in viewers. This study aimed to create a multimodal deep learning
model capable of determining whether a given social media post promotes eating
disorders based on a combination of visual and textual data. A labeled dataset
of Tweets was collected from Twitter, upon which twelve deep learning models
were trained and tested. Based on model performance, the most effective deep
learning model was the multimodal fusion of the RoBERTa natural language
processing model and the MaxViT image classification model, attaining accuracy
and F1 scores of 95.9% and 0.959 respectively. The RoBERTa and MaxViT fusion
model, deployed to classify an unlabeled dataset of posts from the social media
sites Tumblr and Reddit, generated similar classifications as previous research
studies that did not employ artificial intelligence, showing that artificial
intelligence can develop insights congruent to those of researchers.
Additionally, the model was used to conduct a time-series analysis of yet
unseen Tweets from eight Twitter hashtags, uncovering that the relative
abundance of pro-eating disorder content has decreased drastically. However,
since approximately 2018, pro-eating disorder content has either stopped its
decline or risen once more in ampleness
A Deep Learning-Based Automatic Object Detection Method for Autonomous Driving Ships
An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.
Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.
In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated a Sea-object Image Dataset (SID) specifically for this project. Then, by utilizing a pre-trained RetinaNet model on a large-scale object detection dataset named Microsoft COCO, we further fine-tune it on our SID dataset. We focused on sea objects that may potentially cause collisions or other types of maritime accidents. Our final model can effectively detect various types of floating or surrounding objects and classify them into one of the ten predefined significant classes, which are buoy, ship, island, pier, person, waves, rocks, buildings, lighthouse, and fish. Experimental results have demonstrated its good performance