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.
Unsupervised MT struggles with morphologically rich languages.
Discriminative model improves bilingual lexicon induction.
Geometric approach learns bilingual mappings from monolingual embeddings.
Simple framework decouples word alignment and multilingual embedding mapping.
When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hyb…
Paper refines cross-lingual word embeddings using Manhattan norm.
We introduce BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data. Instead it trains directly on monolingual dat…
We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel sentence) to choose a sense for a given word, and a decoder which predicts context words based on the chos…
Geometric approach for unsupervised word embedding alignment.
Improved unsupervised word translation using adversarial autoencoder with cycle consistency and input reconstruction.
The multilingual nature of the world makes translation a crucial requirement today. Parallel dictionaries constructed by humans are a widely-available resource, but they are limited and do not provide enough coverage for good quality translation purposes, due to out-of-vocabulary words and neologisms. This motivates th…
Our research extends the Bilingual Evaluation Understudy (BLEU) evaluation technique for statistical machine translation to make it more adjustable and robust. We intend to adapt it to resemble human evaluation more. We perform experiments to evaluate the performance of our technique against the primary existing evalua…
Inductive Matrix Completion (IMC) is an important class of matrix completion problems that allows direct inclusion of available features to enhance estimation capabilities. These models have found applications in personalized recommendation systems, multilabel learning, dictionary learning, etc. This paper examines a g…
New method identifies latent variables without strong assumptions.
No free lunch theorems show all algorithms perform equally under uniform distribution.
DeepCAM learns convolutional dictionaries for image processing.
Cross-language learning allows us to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning …
Bayesian method improves dictionary learning for complex problems.
Paper improves dictionary learning by addressing local and global coherence issues.
We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficient…
This work learns sparse tensor representations using mixtures of separable dictionaries.
NOODL solves dictionary and coefficient recovery for online learning.
DeepAM optimizes deep neural networks for image super-resolution.
We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing…
Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary. Dictionaries are either available analytically, or can be learned from a suitable training set. While analytic dictionaries permit to capture the global st…
CRsAE auto-encoder recovers convolutional dictionary from noisy signals.
Efficient algorithm selects atoms from dictionaries with complex sparsity constraints.
Paper proposes robust dictionary learning using concave losses.
Paper provides conditions for local recovery of tensor data's Kronecker-structured dictionaries.
Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary learning method adapts an initial dictionary to a particular signal class by iteratively computing an approximate factorization of a training …
Study shows unique sharp local minimum in -minimization for dictionary learning.
A dictionary connects symplectic to contact geometry, with applications to complex and G-structures.
A parallel algorithm learns efficient Kronecker product dictionaries.
Researchers find optimal dictionaries for minimizing average squared coefficients in random vector representations.
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on weighted graphs, an additional design challenge is to incorporate …
Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The availability of abundant training data necessitates the development of efficient, robust and provably good dictionary learning algorithms. Algorithmic stability an…
The paper provides guarantees for an alternating minimization algorithm in dictionary learning.
The paper tackles dictionary learning with almost sure error constraints.
We study the Dictionary Learning (aka Sparse Coding) problem of obtaining a sparse representation of data points, by learning \emph{dictionary vectors} upon which the data points can be written as sparse linear combinations. We view this problem from a geometry perspective as the spanning set of a subspace arrangement,…
Two algorithms converge to dictionary learning with geometric rate for non-uniform data.
The paper proposes a method to learn discriminative multilevel dictionaries for supervised image classification.
New algorithm reduces dictionary learning complexity.
Unified analysis for robust PCA decomposition with sparse components in known dictionaries.
Dictionary learning is a cutting-edge area in imaging processing, that has recently led to state-of-the-art results in many signal processing tasks. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. Determining …
Dictionaries are collections of vectors used for representations of random vectors in Euclidean spaces. Recent research on optimal dictionaries is focused on constructing dictionaries that offer sparse representations, i.e., -optimal representations. Here we consider the problem of finding optimal dictionaries …
This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate s…
A new tree-based method for adaptive dictionary learning.
The kernel least-mean-square (KLMS) algorithm is an appealing tool for online identification of nonlinear systems due to its simplicity and robustness. In addition to choosing a reproducing kernel and setting filter parameters, designing a KLMS adaptive filter requires to select a so-called dictionary in order to get a…