A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
DP-Net uses dynamic programming for efficient deep neural network compression.
Model compression improves dynamic forecasting ensembles while reducing computational costs.
Efficiently compress overparameterized deep models by focusing on low-dimensional learning dynamics.
Let M be a nontrivial compression body without toroidal boundary components. We study the dynamics of the group of outer automorphisms of the fundamental group of M on the PSL(2,C)-character variety of M.
New study reveals how heavy-tailed SGD dynamics lead to compressible neural networks.
This work proves that large models can be compressed significantly without losing performance.
Paper uses SGLD to recover signals from generative models, proving convergence under mild conditions.
New approach uses compressible dynamics to train deep models efficiently.
NeuZip compresses neural network weights to save memory during training and inference.
Computational Fluid Dynamics (CFD) is a hugely important subject with applications in almost every engineering field, however, fluid simulations are extremely computationally and memory demanding. Towards this end, we present Lat-Net, a method for compressing both the computation time and memory usage of Lattice Boltzm…
Model compresses event-like contexts using gated surprise signals.
LEAD algorithm speeds up decentralized optimization with compression.
Paper extends port-Hamiltonian model to include internal energy for compressible and incompressible flow.
Sketching reduces data size for accurate spectral estimation.
Recurrent neural networks (RNNs) achieve cutting-edge performance on a variety of problems. However, due to their high computational and memory demands, deploying RNNs on resource constrained mobile devices is a challenging task. To guarantee minimum accuracy loss with higher compression rate and driven by the mobile r…
Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has de…
Posterior sampling estimator achieves near-optimal recovery guarantees for signals from any prior distribution.
NAC-FL optimizes model updates in FL systems by adapting compression to network congestion.
This work analyzes how different forms of compressibility affect adversarial robustness in neural networks.
LASER compresses recursive model activations by exploiting their low-dimensional structure.
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events. We perform parameter and state estimation using a dynamic compressed sensing framework and develop an efficient solution consisting of two nes…
In this note we survey some recent results for the Euler equations in compressible and incompressible fluid dynamics. The main point of all these theorems is the surprising fact that a suitable variant of Gromov's -principle holds in several cases.
We study the problem of learning associative memory -- a system which is able to retrieve a remembered pattern based on its distorted or incomplete version. Attractor networks provide a sound model of associative memory: patterns are stored as attractors of the network dynamics and associative retrieval is performed by…
A new algorithm improves Bayesian federated learning by reducing communication overhead.
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged co-variate models to study such …
Dynamic model pruning improves performance on deep neural networks without retraining.
This paper analyzes the training dynamics of binary neural networks using information bottleneck.
The Information Plane theory predicts autoencoders do not compress input information.
Prototype for adaptive electron microscopy scans reduces dose and time.
Infrastructure monitoring is critical for safe operations and sustainability. Water distribution networks (WDNs) are large-scale networked critical systems with complex cascade dynamics which are difficult to predict. Ubiquitous monitoring is expensive and a key challenge is to infer the contaminant dynamics from parti…
We discuss algorithms for estimating the Shannon entropy h of finite symbol sequences with long range correlations. In particular, we consider algorithms which estimate h from the code lengths produced by some compression algorithm. Our interest is in describing their convergence with sequence length, assuming no limit…
Efficiently price high-dimensional Bermudan options using tensor compression.
Softmax temperature influences model representation rank and performance.
Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision tasks, but the enormous amount of memory and compute resources required by CNNs pose a challenge in deploying them on constrained devices. Existing compression techniques, while excelling at reducing model sizes, struggle to be comp…
Analysis of Vlasov plasma dynamics using matched pair Lie-Poisson formulation.
Optimizes neural network training by dynamically updating Tucker decomposition ranks.
Recently, researchers proposed various low-precision gradient compression, for efficient communication in large-scale distributed optimization. Based on these work, we try to reduce the communication complexity from a new direction. We pursue an ideal bijective mapping between two spaces of gradient distribution, so th…
To improve the execution speed and efficiency of neural networks in embedded systems, it is crucial to decrease the model size and computational complexity. In addition to conventional compression techniques, e.g., weight pruning and quantization, removing unimportant activations can reduce the amount of data communica…
In many compressive sensing problems today, the relationship between the measurements and the unknowns could be nonlinear. Traditional treatment of such nonlinear relationships have been to approximate the nonlinearity via a linear model and the subsequent un-modeled dynamics as noise. The ability to more accurately ch…
CSGM framework applied to clinical MRI data for robust reconstructions.
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements. The signals are assumed sparse, with unknown support, and evolve over time according to a generic nonlinear dynamical model. Our algorithm, based on recent theoretical results for -$…
An ideal cognitively-inspired memory system would compress and organize incoming items. The Kanerva Machine (Wu et al, 2018) is a Bayesian model that naturally implements online memory compression. However, the organization of the Kanerva Machine is limited by its use of a single Gaussian random matrix for storage. Her…
DGNet solves complex dynamical systems with neural networks and constraints.
Tensor decompositions are powerful tools for large data analytics as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data. One of the most widely studied and used decompositions, especially in data mining and machi…
Paper proposes DCT for efficient hybrid parallel training of large recommendation models.
Paper develops a new fluid flow model with energy exchange through boundaries.
This paper describes a new online convex optimization method which incorporates a family of candidate dynamical models and establishes novel tracking regret bounds that scale with the comparator's deviation from the best dynamical model in this family. Previous online optimization methods are designed to have a total a…
In this paper we present a connection between two dynamical systems arising in entirely different contexts: one in signal processing and the other in biology. The first is the famous Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery while the second is the dynamics of …