36 citations · 197 across the 20 of their papers we have counts for
22 papers
Position: Modular Memory is the Key to Continual Learning Agents
Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov +21
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these…
Quantifying the Role of OpenFold Components in Protein Structure Prediction
Tyler L. Hayes, Giri P. Krishnan
Models such as AlphaFold2 and OpenFold have transformed protein structure prediction, yet their inner workings remain poorly understood. We present a methodology to systematically…
Continual Learning: Applications and the Road Forward
Eli Verwimp, Rahaf Aljundi, Shai Ben-David +17
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting wh…
How Efficient Are Today's Continual Learning Algorithms?
Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes +1
Supervised Continual learning involves updating a deep neural network (DNN) from an ever-growing stream of labeled data. While most work has focused on overcoming catastrophic forg…
SIESTA: Efficient Online Continual Learning with Sleep
Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes +2
In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any dis…
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
Indranil Sur, Zachary Daniels, Abrar Rahman +16
As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adap…
Online Continual Learning for Embedded Devices
Tyler L. Hayes, Christopher Kanan
Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets. However, thi…
Can I see an Example? Active Learning the Long Tail of Attributes and Relations
Tyler L. Hayes, Maximilian Nickel, Christopher Kanan +2
There has been significant progress in creating machine learning models that identify objects in scenes along with their associated attributes and relationships; however, there is…
Disentangling Transfer and Interference in Multi-Domain Learning
Yipeng Zhang, Tyler L. Hayes, Christopher Kanan
Humans are incredibly good at transferring knowledge from one domain to another, enabling rapid learning of new tasks. Likewise, transfer learning has enabled enormous success in m…
Replay in Deep Learning: Current Approaches and Missing Biological Elements
Tyler L. Hayes, Giri P. Krishnan, Maxim Bazhenov +3
Replay is the reactivation of one or more neural patterns, which are similar to the activation patterns experienced during past waking experiences. Replay was first observed in bio…
Selective Replay Enhances Learning in Online Continual Analogical Reasoning
Tyler L. Hayes, Christopher Kanan
In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
Self-Supervised Training Enhances Online Continual Learning
Jhair Gallardo, Tyler L. Hayes, Christopher Kanan
In continual learning, a system must incrementally learn from a non-stationary data stream without catastrophic forgetting. Recently, multiple methods have been devised for increme…
Improved Robustness to Open Set Inputs via Tempered Mixup
Ryne Roady, Tyler L. Hayes, Christopher Kanan
Supervised classification methods often assume that evaluation data is drawn from the same distribution as training data and that all classes are present for training. However, rea…
RODEO: Replay for Online Object Detection
Manoj Acharya, Tyler L. Hayes, Christopher Kanan
Humans can incrementally learn to do new visual detection tasks, which is a huge challenge for today's computer vision systems. Incrementally trained deep learning models lack back…
Do We Need Fully Connected Output Layers in Convolutional Networks?
Zhongchao Qian, Tyler L. Hayes, Kushal Kafle +1
Traditionally, deep convolutional neural networks consist of a series of convolutional and pooling layers followed by one or more fully connected (FC) layers to perform the final c…
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
Ryne Roady, Tyler L. Hayes, Ronald Kemker +2
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, dep…
REMIND Your Neural Network to Prevent Catastrophic Forgetting
Tyler L. Hayes, Kushal Kafle, Robik Shrestha +2
People learn throughout life. However, incrementally updating conventional neural networks leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the…
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis
Tyler L. Hayes, Christopher Kanan
When an agent acquires new information, ideally it would immediately be capable of using that information to understand its environment. This is not possible using conventional dee…
Memory Efficient Experience Replay for Streaming Learning
Tyler L. Hayes, Nathan D. Cahill, Christopher Kanan
In supervised machine learning, an agent is typically trained once and then deployed. While this works well for static settings, robots often operate in changing environments and m…
Compassionately Conservative Balanced Cuts for Image Segmentation
Nathan D. Cahill, Tyler L. Hayes, Renee T. Meinhold +1
The Normalized Cut (NCut) objective function, widely used in data clustering and image segmentation, quantifies the cost of graph partitioning in a way that biases clusters or segm…
Efficiently Computing Piecewise Flat Embeddings for Data Clustering and Image Segmentation
Renee T. Meinhold, Tyler L. Hayes, Nathan D. Cahill
Image segmentation is a popular area of research in computer vision that has many applications in automated image processing. A recent technique called piecewise flat embeddings (P…