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Found 8 papers, 3 papers with code
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains
We examine how learning is affected by varying the prior distribution over Markov chains, and consider the generalization of our in-context learning of Markov chains (ICL-MC) task to $n$-grams for $n > 2$.
On the Robustness of Neural Collapse and the Neural Collapse of Robustness
Neural Collapse refers to the curious phenomenon in the end of training of a neural network, where feature vectors and classification weights converge to a very simple geometrical arrangement (a simplex).
Kernels, Data & Physics
Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches.
A Tale of Two Circuits: Grokking as Competition of Sparse and Dense Subnetworks
Grokking is a phenomenon where a model trained on an algorithmic task first overfits but, then, after a large amount of additional training, undergoes a phase transition to generalize perfectly.
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?
The adversarial vulnerability of neural nets, and subsequent techniques to create robust models have attracted significant attention; yet we still lack a full understanding of this phenomenon.
Can we achieve robustness from data alone?
In parallel, we revisit prior work that also focused on the problem of data optimization for robust classification \citep{Ily+19}, and show that being robust to adversarial attacks after standard (gradient descent) training on a suitable dataset is more challenging than previously thought.
Extracting Finite Automata from RNNs Using State Merging
One way to interpret the behavior of a blackbox recurrent neural network (RNN) is to extract from it a more interpretable discrete computational model, like a finite state machine, that captures its behavior.
The NTK Adversary: An Approach to Adversarial Attacks without any Model Access
In particular, in the regime where the Neural Tangent Kernel theory holds, we derive a simple, but powerful strategy for attacking models, which in contrast to prior work, does not require any access to the model under attack, or any trained replica of it for that matter.
