arXiv research

New method uses binary quadratic forms to classify Seifert surfaces in 4-ball.

We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the composition of a proper loss with a link function. We characterise when margin losses can be proper composite losses, explicitly show how to determ…

Generative model learns to compose images of objects from different distributions.

We use mathematical induction to prove that the horizontal composition in the class of coherently diagonal complexes is indeed a binary operation. That is to say, the embedding of two coherently diagonal complexes in an alternating planar diagram produces a coherently diagonal complex.

We introduce the hierarchical compositional network (HCN), a directed generative model able to discover and disentangle, without supervision, the building blocks of a set of binary images. The building blocks are binary features defined hierarchically as a composition of some of the features in the layer immediately be…

Learning compact and interpretable representations is a very natural task, which has not been solved satisfactorily even for simple binary datasets. In this paper, we review various ways of composing experts for binary data and argue that competitive forms of interaction are best suited to learn low-dimensional represe…

In this paper, we propose a compositional nonparametric method in which a model is expressed as a labeled binary tree of $2k+1$ nodes, where each node is either a summation, a multiplication, or the application of one of the $q$ basis functions to one of the $p$ covariates. We show that in order to recover a labeled bi…

Paper proposes HTAF for stable training of binary neural networks.

Deep networks learn sparse hierarchical features without CoD.

Paper improves gas species identification in complex mixtures using neural networks.

Efficient binarized algorithm detects seizures from iEEG with one-shot learning.

This work improves task specification learning from demonstrations using maximum causal entropy.

New method for causal inference with complex treatment compositions.

The paper constructs new algebraic structures from Lie algebras and ternary Nambu-Lie algebras, leading to Yang-Baxter operators.

PolyILR: A Tree-Structured Orthonormal Decomposition of Compositional Data

New binary loss functions improve density ratio estimation accuracy.

This work simplifies proximal mapping for low-rank norms.

Study develops efficient algorithm for probabilistic penetration response of composite plates.

Geometric analysis of ROC and PR curves for binary classification.

The goal of online prediction with expert advice is to find a decision strategy which will perform almost as well as the best expert in a given pool of experts, on any sequence of outcomes. This problem has been widely studied and $O(\sqrt{T})$ and $O(\log{T})$ regret bounds can be achieved for convex losses (\cite{zin…

Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic f…

New method studies moving points on curves using rotating frames.

Novel oracle-type inequality for logistic loss in DNNs achieves sharp convergence rates.

The paper categorizes and analyzes various event-linked perpetual futures contracts.

This research tackles group fairness in predictive process monitoring by ensuring predictions are independent of sensitive group membership.

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings …

Deep neural networks achieve optimal classification rates in high dimensions.

This work is an analytical and numerical study of the composition of several fractals into one and of the relation between the composite dimension and the dimensions of the component fractals. In the case of composition of standard IFS with segments of equal size, the composite dimension can be expressed as a function …

Kernel-based learning predicts ICU escalation from COVID-19 chest X-rays.

New framework resolves central limit behavior in differential privacy.

Unified framework for estimating density ratios across multiple distributions.

New geometric approach for analyzing compositional data like gut microbiomes.

Study on deep neural networks using branching processes and Mehler's formula.

Uses art composition attributes to guide CycleGAN image translation.

We establish conditions for compositional generalization in machine learning.

This paper proves hyperbolicity of virtual knot compositions.

Develops methods for causal inference in compositional data using instrumental variables.

In classical field theory, the composite fibred manifolds Y -> Z -> X provides the adequate mathematical formulation of gauge models with broken symmetries, e.g., the gauge gravitation theory. This work is devoted to connections on composite fibred manifolds. In particular, we get the horizontal splitting of the vertic…

The p-index improves investment performance for NYSE stocks but not for SSE stocks.

Model predicts composite structures assembly quality with input uncertainty.

New algorithm reduces complexity for optimizing complex machine learning tasks.

This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.

Study challenges neural models in compositional learning tasks.

SCL discovers compositional structures in analogical reasoning tasks.

Composite neural network improves PM2.5 prediction.

New filters match advanced composition for adaptive privacy, with practical constants.

Paper develops momentum schemes with variance reduction for non-convex composition optimization.

Extends knockoff filter for composite null hypotheses in variable selection.