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.
New method simplifies Bayesian analysis for categorical data.
Deep belief networks can approximate any multivariate density with binary hidden units.
New method for estimating high-dimensional binary time series coefficients.
A new test method improves goodness-of-fit tests for copulas.
Efficiently poisons offline RLHF models by flipping preference labels.
RBMs model binary interactions with hidden node activation effects.
We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-preserving binary code. This code can be used to apply fast and memory-efficient approximation to nearest-neighbor searches. Our framework is …
This paper considers binomial approximation of continuous time stochastic processes. It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by binomial processes, i.e., by processes with fixed size binary increments at sampling points. …
In this paper, we prove a Donsker type approximation theorem for the Rosenblatt process, which is a selfsimilar stochastic process exhibiting long range dependence. By using numerical results and simulated data, we show that this approximation performs very well. We use this result to construct a binary market model dr…
Binary Neural Networks (BNNs) have been garnering interest thanks to their compute cost reduction and memory savings. However, BNNs suffer from performance degradation mainly due to the gradient mismatch caused by binarizing activations. Previous works tried to address the gradient mismatch problem by reducing the disc…
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approac…
We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bit…
New exact tests detect changepoints in binary and count data, especially when normal approximations fail.
The paper proposes a least squares method for binary compressive sampling with low intrinsic dimension signals.
For the problem of binary linear classification and feature selection, we propose algorithmic approaches to classifier design based on the generalized approximate message passing (GAMP) algorithm, recently proposed in the context of compressive sensing. We are particularly motivated by problems where the number of feat…
Paper studies binary random projections with controllable sparsity patterns for computational and accuracy advantages.
This paper presents novel mixed-type Bayesian optimization (BO) algorithms to accelerate the optimization of a target objective function by exploiting correlated auxiliary information of binary type that can be more cheaply obtained, such as in policy search for reinforcement learning and hyperparameter tuning of machi…
Combines VI and EP for better Gaussian process hyperparameter learning.
Binary representation is desirable for its memory efficiency, computation speed and robustness. In this paper, we propose adjustable bounded rectifiers to learn binary representations for deep neural networks. While hard constraining representations across layers to be binary makes training unreasonably difficult, we s…
Loss-calibrated EP improves Bayesian decision-making by focusing on utility-sensitive posterior approximations.
BEGIN network models binary data without parametric assumptions.
ConvResNets approximate Besov functions and classify on low-dimensional manifolds.
We improve recently published results about resources of Restricted Boltzmann Machines (RBM) and Deep Belief Networks (DBN) required to make them Universal Approximators. We show that any distribution p on the set of binary vectors of length n can be arbitrarily well approximated by an RBM with k-1 hidden units, where …
New method for estimating gradients in stochastic binary networks.
Reintroduces straight-through estimators for binary neural networks.
Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, etc. Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matri…
A note proves the binary perceptron's capacity is less than 0.847.
Novel approximation hierarchy for sparse quadratic programs.
A new method prunes neural networks efficiently without losing effectiveness.
Confidence intervals improve evaluation of binary prediction rules in data mining.
Introduces a differentiable approximation to the zero-one loss.
Large-scale deep neural networks are both memory intensive and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated in both industry and academia. Specific forms of binary neural networks (BNNs) and sto…
Neural networks approximate and estimate binary classifiers with polynomial input dependence.
Reverse annealing boosts quantum matrix factorization performance.
Quantum algorithm improves sparse vector recovery from noisy measurements.
Modern online platforms rely on effective rating systems to learn about items. We consider the optimal design of rating systems that collect binary feedback after transactions. We make three contributions. First, we formalize the performance of a rating system as the speed with which it recovers the true underlying ran…
Unified framework approximates gradient descent's implicit bias in high dimensions.
Binary hashing is a well-known approach for fast approximate nearest-neighbor search in information retrieval. Much work has focused on affinity-based objective functions involving the hash functions or binary codes. These objective functions encode neighborhood information between data points and are often inspired by…
Computations based on explicit 4-periodic resolutions are given for the cohomology of the finite groups G known to act freely on S^3, as well as the cohomology rings of the associated 3-manifolds (spherical space forms) M = S^3/G. Chain approximations to the diagonal are constructed, and explicit contracting homotopies…
A scalable ROC-SVM variant reduces training time for imbalanced binary classification.
ENTED efficiently decomposes binary and count tensors using nonparametric Gaussian processes.
Study on CNNs' learning rates and approximation capacities.
We propose a mixture of latent trait models with common slope parameters (MCLT) for model-based clustering of high-dimensional binary data, a data type for which few established methods exist. Recent work on clustering of binary data, based on a -dimensional Gaussian latent variable, is extended by incorporating com…
We establish upper bounds for the minimal number of hidden units for which a binary stochastic feedforward network with sigmoid activation probabilities and a single hidden layer is a universal approximator of Markov kernels. We show that each possible probabilistic assignment of the states of output units, given t…
Proposes an alternative method to train RBMs with binary synapses using Bayesian learning rule.
Just as semantic hashing can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying b…
Proposes a method to estimate time-dependent probability density functions using binary classifiers.
Study binary hypothesis testing with privacy and communication constraints.