Two new methods improve coherence modeling without complex machine translation.
problem Improving neural coherence modeling for better sentence ordering.
method Two novel methods combining regression and context concatenation.
result Achieves state-of-the-art Kendall-tau and positional accuracy scores.
New technique stabilizes singular values in concatenated matrices.
problem How singular values of concatenated matrices relate to individual components.
method Developed perturbation technique extending classical results to concatenated matrices.
result Dominant singular values remain stable under small perturbations in submatrices.
FCMSC combines multi-view data through feature concatenation for improved clustering.
problem Clustering multi-view data with diverse and sometimes incompatible views.
method FCMSC concatenates multi-view data, integrates l2,1-norm, and uses graph regularization to explore consensus and complementary information. result FCMSC outperforms state-of-the-art multi-view clustering methods on six real-world datasets.
i-DenseNets improve parameter efficiency and performance in density estimation.
problem Improving parameter efficiency and performance in density estimation models.
method Invertible Dense Networks (i-DenseNets) with learnable weighted concatenation and Concatenated LipSwish activation function.
result i-DenseNets outperform Residual Flows and other flow-based models in bits per dimension.
We develop a method to describe laws of random surfaces using surface holonomy.
problem Describing laws of random surfaces with structure.
method Introduce surface holonomy and develop expected surface developments.
result Expected surface development provides a structured description of random surface laws.
ContextFlow++ improves generative models by conditioning on mixed-variable contexts.
problem Lack of effective methods for context conditioning in flow-based generative models.
method Proposes ContextFlow++ with additive conditioning and mixed-variable architecture.
result ContextFlow++ achieves higher performance metrics and faster training.
Transformers can scale both context and task, but MLPs can only scale task.
problem Understanding and scaling In-Context Learning in transformers.
method Simplified transformer architecture, feature map, and MLP combination.
result Simplified transformer can perform ICL and context-scaling but not task-scaling.
SVM with local features improves human action recognition.
problem Improving human action recognition in videos.
method Local appearance and motion features extracted using CNNs, concatenated, and used with SVM for classification.
result SVM with local features outperforms previous methods on benchmark datasets.
This is an investigation of the role of shuffling and concatenating in the theory of graph drawing. A simple syntactic description of these and related operations is proved complete in the context of finite partial orders, as general as possible. An explanation based on that is given for a previously investigated colla…
Invertible DenseNets improve model efficiency and performance.
problem Improving model efficiency and performance in neural networks.
method Enforcing invertibility in DenseNets by satisfying the Lipschitz constraint and proposing a learnable concatenation.
result i-DenseNets outperform Residual Flows in negative log-likelihood on various datasets.
Enhanced ODT with Feature Concatenation boosts learning efficiency.
problem Insufficient learning efficiency of ODT due to linear projections not being transmitted to child nodes.
method Feature Concatenation ( exttt{FC-ODT}) to transmit linear projections along decision paths.
result Experiments show exttt{FC-ODT} outperforms state-of-the-art decision trees with a limited tree depth.
The paper proposes a new algorithm to estimate personalized treatment effects.
problem Estimating personalized heterogeneous treatment effects.
method Concatenation and augmentation of feature vectors, outcome regression function construction.
result The proposed algorithm outperforms T-learner and X-learner in various simulation experiments.
Adding supplementary axes improves neural network learnability and accuracy.
problem Overfitting and computational cost in deep neural networks.
method Analysis of a simple MLP model and comparison with and without supplementary information.
result Neural networks with supplementary axes show more robust and accurate training results.
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a lon…
CAMul forecasts with calibrated and accurate multi-view time-series data.
problem Combining diverse data sources for reliable time-series forecasting.
method CAMul integrates multi-modal data views dynamically, assigning importance based on context.
result CAMul outperforms state-of-the-art models by 25% in accuracy and calibration.
The paper computes a knot's Kauffman bracket polynomial using recursive concatenation of a 4-tangle shadow.
problem Computing the Kauffman bracket polynomial for complex knots.
method Recursive concatenation of a 4-tangle shadow, followed by a closure operation and polynomial computation.
result A method to compute the Kauffman bracket polynomial for knots formed from 4-tangle shadows.
This paper constructs GCM hypersurfaces in Kerr spacetimes.
problem Extending the Kerr family stability proof to full stability.
method Concatenating a 1-parameter family of GCM spheres by solving an ODE system.
result Removes symmetry restrictions in GCM procedure.
The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GA…
Paper introduces multi-scale methods to improve CATE estimation from EO data.
problem Challenges in balancing fine-grained and contextual information in EO-based causal inference.
method Multi-Scale Representation Concatenation, combining Vision Transformer and Causal Forests.
result Multi-scale approach captures effect heterogeneity better than single-scale models.
MCN improves deep neural networks by bettering local minima and generalizing well.
problem Bad local minima and poor generalization in deep neural networks.
method Introducing Maximum-and-Concatenation Networks (MCN) to eliminate bad local minima and improve generalization.
result MCN can autonomously improve local minima's goodness by increasing network depth.
Polynomial fusion layer improves speech-driven facial animation.
problem Recent facial synthesis relies on low-dimensional representations and concatenation, ignoring higher-order interactions.
method Proposes a polynomial fusion layer to model higher-order interactions of facial encodings.
result Demonstrates improved video quality, audiovisual synchronisation, and blink generation.
There are a multitude of methods to perform multi-set correlated component analysis (MCCA), including some that require iterative solutions. The methods differ on the criterion they optimize and the constraints placed on the solutions. This note focuses perhaps on the simplest version, which can be solved in a single s…
New unsupervised speaker adaptation method for speech synthesis.
problem Adapting speech synthesis to new speakers with minimal data.
method Concatenating audio and text inputs, proposing new training schemes.
result Improves adaptation to unseen speakers and multi-speaker modeling.
This paper extends the construction of invariants for virtual knots to virtual long knots and introduces two new invariant modules of virtual long knots. Several interesting features are described that distinguish virtual long knots from their classical counterparts with respect to their symmetries and the concatenatio…
Paper uses virtual big data to improve autoencoder training and address imbalanced data classification.
problem Imbalanced data classification and autoencoder over-fitting.
method Cross-concatenation using Virtual Big Data.
result Cross-concatenation method effectively balances imbalanced class distributions.
Embeddings of lab test codes improve mortality prediction and preserve ordinality.
problem Improving mortality prediction using lab test embeddings.
method Training embeddings for LOINC codes and their concatenations with abnormality symbols, evaluating performance on mortality prediction tasks.
result Embeddings of lab test codes improve mortality prediction and preserve ordinality.
The paper quantizes concatenated noisy vectors to a common cluster center, improving performance over naive methods.
problem Clustering concatenated noisy vectors from multiple sources.
method Asymptotic analysis of weighted sum of distances to a common cluster center.
result The clustering approach outperforms naive methods in terms of average distortion.
Deep Transformed Gaussian Processes extend TGPs with variational inference for scalable multi-layer modeling.
problem Flexible modeling of complex data distributions.
method DTGPs are a multi-layer model of TGPs using variational inference for scalability.
result DTGPs achieve good scalability and performance in multiple regression datasets.
In the context of large financial markets we formulate the notion of \emph{no asymptotic free lunch with vanishing risk} (NAFLVR), under which we can prove a version of the fundamental theorem of asset pricing (FTAP) in markets with an (even uncountably) infinite number of assets, as it is for instance the case in bond…
Concrete description of infinite order cork automorphism.
problem Describing the infinite order loose-cork automorphism.
method Concatenating the defining ribbon disk by an infinite order isotopy.
result Concrete description of the infinite order cork automorphism.
Study of Sard problem in Carnot groups using dynamical systems.
problem Sard problem in sub-Riemannian Carnot groups.
method Dynamical-systems approach to study singular curves.
result Positively answer the Sard problem in some Carnot groups.
Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large…
We compute the Kauffman bracket polynomial of the three-lead Turk's head, the chain sinnet and the figure-eight chain shadow diagrams. Each of these knots can in fact be constructed by repeatedly concatenating the same 3-tangle, respectively, then taking the closure. The bracket is then evaluated by expressing the stat…
We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects. Introducing a measure of correspondence between spatial decompositions of rest …
We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic repr…
Embeds complex 3-manifolds into symplectic space.
problem Embedding non-orientable 3-manifolds into symplectic spaces.
method Lagrangian embedding of specific 3-manifolds into standard symplectic 6-space.
result Minimal Maslov number of embedding is 1.
New metric reveals how topology affects deep network performance.
problem Understanding how topology influences deep network performance.
method Introduced NN-Mass metric to quantify gradient propagation and model performance.
result NN-Mass identifies models with similar accuracy but different sizes/compute requirements.
The paper introduces surface signatures for irregular surfaces and rough surfaces.
problem Characterizing and integrating highly irregular paths and surfaces.
method Introducing surface signatures and proving extension theorems.
result Surface signatures are universal for surface holonomy and rough surfaces.
A coordinate-free proof of the Maximum Principle is provided in the specific case of an optimal control problem with fixed time. Our treatment heavily relies on a special notion of variation of curves that consist of a concatenation of integral curves of time-dependent vector fields with unit time component, and on the…
New sparsification technique for SGD reduces communication costs.
problem High communication costs in distributed SGD for large-scale models.
method Statistical estimation model for sparsity and skewness of stochastic gradients.
result Concatenated top-k and random-k sparsification outperforms individual methods.
ODE trajectories become abnormal curves in Carnot groups.
problem Understanding abnormal curves in Carnot groups.
method Explicit construction of covectors for abnormal curves.
result Polynomial ODE trajectories lift to abnormal curves in Carnot groups.
EEG signals enhance speaker verification system robustness.
problem Improving speaker verification in noisy environments.
method Used end-to-end deep learning model with EEG and speech features.
result EEG signals improve speaker verification robustness, especially in noisy conditions.
The paper constructs minimizers for deep learning networks and analyzes their geometric structure.
problem Underparametrized deep learning networks and their minimizers.
method Direct construction of minimizers without gradient descent, considering specific settings.
result Explicit family of minimizers for the global minimum and a set of degenerate local minima.
We discuss theoretical aspects of the product rule for classification problems in supervised machine learning for the case of combining classifiers. We show that (1) the product rule arises from the MAP classifier supposing equivalent priors and conditional independence given a class; (2) under some conditions, the pro…
The paper is divided in 2 parts. The first part is the original paper of the second and third authors arXiv:1202.5442v2. The second part is an erratum/addendum written in english and concatenated at the end of the former paper. In the erratum/addentum, we amend Theorems 1.3 and 1.11 of arXiv:1202.5442v2: Finitude géomé…
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…
Better neural arithmetic logic units improve cell counting model generalization.
problem Neural networks struggle with high cell counts outside training data range.
method Introduced Neural Arithmetic Logic Units (NALU) for arithmetic operations in existing architectures.
result Improved cell counting accuracy for higher numeric ranges with better generalization.
New polynomial invariants defined for long virtual knots.
problem Defining and studying polynomial invariants for long virtual knots.
method Intersection numbers of cycles on a closed surface, considering crossing order.
result Intersection polynomials are finite-type invariants of degree two under crossing changes, but not under virtualizations.