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.
LLMs compress financial texts, but distort decision-making.
We compress large neural networks for quick adaptation to specific contexts.
New techniques save bits in image compression with upsampling.
Model compresses event-like contexts using gated surprise signals.
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed. While compressing deep neural networks has of late assumed great importance to overcome this drawback, little work has been done to address this problem in the conte…
FibQuant improves KV-cache compression for long-context inference.
We study and provide exposition to several phenomena that are related to the perceptron's compression. One theme concerns modifications of the perceptron algorithm that yield better guarantees on the margin of the hyperplane it outputs. These modifications can be useful in training neural networks as well, and we demon…
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling …
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible. At encoder side, we fine-tune a pre-trained artifact removal network on target data by using a compression objective applied on the weight-u…
Bayesian Attention Networks compress data by focusing on key training samples.
A framework infers causal direction from symbolic sequences using compression measures.
Single model corrects JPEG artifacts for various compression settings.
We study compressing empirical measures in finite RKHSs using convex optimization.
Compressing word embeddings is important for deploying NLP models in memory-constrained settings. However, understanding what makes compressed embeddings perform well on downstream tasks is challenging---existing measures of compression quality often fail to distinguish between embeddings that perform well and those th…
Compressed LLM embeddings improve noisy regression tasks without overfitting.
WoodFisher improves neural network compression efficiency and accuracy.
Deep Neural Networks (DNNs) have recently been achieving state-of-the-art performance on a variety of computer vision related tasks. However, their computational cost limits their ability to be implemented in embedded systems with restricted resources or strict latency constraints. Model compression has therefore been …
A new method compresses conditional distributions of labelled data.
This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix $\Ab$ an…
We consider a decomposition method for compressive streaming data in the context of online compressive Robust Principle Component Analysis (RPCA). The proposed decomposition solves an - cluster-weighted minimization to decompose a sequence of frames (or vectors), into sparse and low-rank components, from com…
CSDM integrates compressed sensing into diffusion models for faster data generation.
The paper uses information geometry to analyze model compression techniques, focusing on operator factorization.
Midicoth compresses online probability estimates by correcting prior smoothing biases.
This paper compresses neural networks by permuting and quantizing weights.
Unified bounds linking compressibility, fractal dimensions, and mutual information.
Optimizes MCMC output compression by selecting a subset of states.
A field known as Compressive Sensing (CS) has recently emerged to help address the growing challenges of capturing and processing high-dimensional signals and data sets. CS exploits the surprising fact that the information contained in a sparse signal can be preserved in a small number of compressive (or random) linear…
More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Teacher-student compression (TSC), also known as distillation, alleviates this burden by training a less expensive student model to mimic the expensive teac…
Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches suffer from limited bandwidth of the network, we propose the use of communication compression in the decentral…
This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypot…
Express improves causal attention guarantees for language models.
Investigates principles of generalization in list learning, refutes sample compression conjecture.
A two-step approach efficiently selects hyperparameters for FCMs.
Paper proposes Nyström sketches for better adaptive compressive learning.
This paper offers an overview of neural network compression techniques.
This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the len…
New method improves generalization of VAEs by reducing overfitting.
Understanding how funding and 4H context regulate crypto markets.
New approach uses compressible dynamics to train deep models efficiently.
Matrix sketching is a recently developed data compression technique. An input matrix A is efficiently approximated with a smaller matrix B, so that B preserves most of the properties of A up to some guaranteed approximation ratio. In so doing numerical operations on big data sets become faster. Sketching algorithms gen…
A new framework uses directed information to efficiently select context chunks.
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…
Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection exists between methods for communication reduction and privacy preservation in the context of distributed machine learning. In particular, we…
We introduce a new parameterization method for deep learning layers using spectral tensor train decomposition.
Learning parameters from voluminous data can be prohibitive in terms of memory and computational requirements. We propose a "compressive learning" framework where we estimate model parameters from a sketch of the training data. This sketch is a collection of generalized moments of the underlying probability distributio…
Gaussian Process upsampling boosts OCR accuracy from low-res images.
Flexible framework compresses models using LC algorithm.
Reduces multiclass and regression compression schemes to binary ones.