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Found 17 papers, 7 papers with code
Universal simulation of Markovian quantum dynamics
Although the conditions for performing arbitrary unitary operations to simulate the dynamics of a closed quantum system are well understood, the same is not true of the more general class of quantum operations (also known as superoperators) corresponding to the dynamics of open quantum systems.
Quantum Physics
Quantum Walks On Graphs
We set the ground for a theory of quantum walks on graphs- the generalization of random walks on finite graphs to the quantum world.
Quantum Physics
Quantum random walks - an introductory overview
This article aims to provide an introductory survey on quantum random walks.
Quantum Physics Data Structures and Algorithms
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
Neural network pruning is a fruitful area of research with surging interest in high sparsity regimes.
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.
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.
ImpressLearn: Continual Learning via Combined Task Impressions
Starting from existing work on network masking (Wortsman et al., 2020), we show that simply learning a linear combination of a small number of task-specific supermasks (impressions) on a randomly initialized backbone network is sufficient to both retain accuracy on previously learned tasks, as well as achieve high accuracy on unseen tasks.
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.
Wavelets Beat Monkeys at Adversarial Robustness
2) Replacing the front-end VOneBlock by an off-the-shelf parameter-free Scatternet followed by simple uniform Gaussian noise can achieve much more substantial adversarial robustness without adversarial training.
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.
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).
Discovering Galaxy Features via Dataset Distillation
In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity.
Deconstructing the Goldilocks Zone of Neural Network Initialization
The second-order properties of the training loss have a massive impact on the optimization dynamics of deep learning models.
A Tale of Tails: Model Collapse as a Change of Scaling Laws
We discover a wide range of decay phenomena, analyzing loss of scaling, shifted scaling with number of generations, the ''un-learning" of skills, and grokking when mixing human and synthesized data.
Model Collapse Demystified: The Case of Regression
In the era of large language models like ChatGPT, the phenomenon of "model collapse" refers to the situation whereby as a model is trained recursively on data generated from previous generations of itself over time, its performance degrades until the model eventually becomes completely useless, i. e the model collapses.
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors
Machine learning models often perform poorly under subpopulation shifts in the data distribution.
Robust Data Pruning: Uncovering and Overcoming Implicit Bias
However, little is known about its impact on classification bias of the trained models.
