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Mechanistic design and scaling of hybrid architectures
M Poli, AW Thomas, E Nguyen, P Ponnusamy… - ar** times, and high compute costs associated with at-scale …
Understanding and Minimising Outlier Features in Transformer Training
Abstract Outlier Features (OFs) are neurons whose activation magnitudes significantly
exceed the average over a neural network's (NN) width. They are well known to emerge …
exceed the average over a neural network's (NN) width. They are well known to emerge …
Understanding and minimising outlier features in neural network training
Outlier Features (OFs) are neurons whose activation magnitudes significantly exceed the
average over a neural network's (NN) width. They are well known to emerge during standard …
average over a neural network's (NN) width. They are well known to emerge during standard …
Towards a Flexible and High-Fidelity Approach to Distributed DNN Training Emulation
We propose NeuronaBox, a flexible, user-friendly, and high-fidelity approach to emulate
DNN training workloads. We argue that to accurately observe performance, it is possible to …
DNN training workloads. We argue that to accurately observe performance, it is possible to …
Entropy-Guided Attention for Private LLMs
The pervasiveness of proprietary language models has raised critical privacy concerns,
necessitating advancements in private inference (PI), where computations are performed …
necessitating advancements in private inference (PI), where computations are performed …
AERO: Softmax-Only LLMs for Efficient Private Inference
The pervasiveness of proprietary language models has raised privacy concerns for users'
sensitive data, emphasizing the need for private inference (PI), where inference is performed …
sensitive data, emphasizing the need for private inference (PI), where inference is performed …
ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models
LayerNorm is a critical component in modern large language models (LLMs) for stabilizing
training and ensuring smooth optimization. However, it introduces significant challenges in …
training and ensuring smooth optimization. However, it introduces significant challenges in …
Testing knowledge distillation theories with dataset size
The concept of knowledge distillation (KD) describes the training of a student model with a
teacher model and is a widespread technique in deep learning. However, it is still not clear …
teacher model and is a widespread technique in deep learning. However, it is still not clear …
Compositional visual reasoning and generalization with neural networks
A Stanić - 2024 - folia.unifr.ch
Deep neural networks (NNs) recently revolutionized the field of Artificial Intelligence, making
great progress in computer vision, natural language processing, complex game play …
great progress in computer vision, natural language processing, complex game play …