Grounding neuroscience in behavioral changes using artificial neural networks
GW Lindsay - Current opinion in neurobiology, 2024 - Elsevier
Connecting neural activity to function is a common aim in neuroscience. How to define and
conceptualize function, however, can vary. Here I focus on grounding this goal in the specific …
conceptualize function, however, can vary. Here I focus on grounding this goal in the specific …
[HTML][HTML] A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Current deep learning methods are regarded as favorable if they empirically perform well on
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual …
Self-supervised video pretraining yields robust and more human-aligned visual representations
N Parthasarathy, SM Eslami… - Advances in Neural …, 2023 - proceedings.neurips.cc
Humans learn powerful representations of objects and scenes by observing how they evolve
over time. Yet, outside of specific tasks that require explicit temporal understanding, static …
over time. Yet, outside of specific tasks that require explicit temporal understanding, static …
Curriculum learning with infant egocentric videos
S Sheybani, H Hansaria, J Wood… - Advances in Neural …, 2024 - proceedings.neurips.cc
Infants possess a remarkable ability to rapidly learn and process visual inputs. As an infant's
mobility increases, so does the variety and dynamics of their visual inputs. Is this change in …
mobility increases, so does the variety and dynamics of their visual inputs. Is this change in …
Artificial neural network language models align neurally and behaviorally with humans even after a developmentally realistic amount of training
Artificial neural networks have emerged as computationally plausible models of human
language processing. A major criticism of these models is that the amount of training data …
language processing. A major criticism of these models is that the amount of training data …
Learning high-level visual representations from a child's perspective without strong inductive biases
AE Orhan, BM Lake - Nature Machine Intelligence, 2024 - nature.com
Young children develop sophisticated internal models of the world based on their visual
experience. Can such models be learned from a child's visual experience without strong …
experience. Can such models be learned from a child's visual experience without strong …
Spikformer v2: Join the high accuracy club on imagenet with an snn ticket
Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the
challenge of limited performance. The self-attention mechanism, which is the cornerstone of …
challenge of limited performance. The self-attention mechanism, which is the cornerstone of …
Not all semantics are created equal: Contrastive self-supervised learning with automatic temperature individualization
In this paper, we aim to optimize a contrastive loss with individualized temperatures in a
principled and systematic manner for self-supervised learning. The common practice of …
principled and systematic manner for self-supervised learning. The common practice of …
Self-supervised video pretraining yields human-aligned visual representations
Humans learn powerful representations of objects and scenes by observing how they evolve
over time. Yet, outside of specific tasks that require explicit temporal understanding, static …
over time. Yet, outside of specific tasks that require explicit temporal understanding, static …
Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training
Artificial neural networks have emerged as computationally plausible models of human
language processing. A major criticism of these models is that the amount of training data …
language processing. A major criticism of these models is that the amount of training data …