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.
Sharp error bounds derived for bidirectional GANs without restrictive assumptions.
A fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially …
We propose a novel training procedure for improving the performance of generative adversarial networks (GANs), especially to bidirectional GANs. First, we enforce that the empirical distribution of the inverse inference network matches the prior distribution, which favors the generator network reproducibility on the se…
TimeVQVAE uses VQ for better time series generation.
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic feature…
In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss function as an additional component of the objective function used to train discriminative data representation in the latent space of the BiGAN m…
For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework. VHE…
Generative adversarial networks (GANs) are capable of producing high quality image samples. However, unlike variational autoencoders (VAEs), GANs lack encoders that provide the inverse mapping for the generators, i.e., encode images back to the latent space. In this work, we consider adversarially learned generative mo…
Spectral normalization stabilizes GANs by controlling gradient explosion and vanishing.
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been demonstrated empirically, with compelling results showing that the latent space of such generators captures semantic variation in the…
The paper improves GANs' theoretical guarantees for low-dimensional data.
We propose an adversarial learning approach for generating multi-turn dialogue responses. Our proposed framework, hredGAN, is based on conditional generative adversarial networks (GANs). The GAN's generator is a modified hierarchical recurrent encoder-decoder network (HRED) and the discriminator is a word-level bidirec…
A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL). It consists of training an undirected neural network to map input to output and vice-versa; therefore it can produce a classifier in one direction, and a generator in the opposite direction for the same data. The lear…
Bidirectional attention is shown to be equivalent to a continuous bag of words model with mixture-of-experts.
Recent work has shown that topological enhancements to recurrent neural networks (RNNs) can increase their expressiveness and representational capacity. Two popular enhancements are stacked RNNs, which increases the capacity for learning non-linear functions, and bidirectional processing, which exploits acausal informa…
Bidirectional VAE reduces parameters and improves image tasks.
Bi-GAN model for imputing and predicting irregular time-series data.
This work proposes a new method to match distributions across different spaces using cycle-consistent maps.
New bidirectional model predicts magnetohydrodynamics fields and estimates uncertainty.
Neural sequence generation is typically performed token-by-token and left-to-right. Whenever a token is generated only previously produced tokens are taken into consideration. In contrast, for problems such as sequence classification, bidirectional attention, which takes both past and future tokens into consideration, …
Study develops a new algorithm for assessing clinical trial abstracts.
New method estimates bidirectional causal effects in large-scale systems.
Paper proposes using LSTM for LSH-based sequence alignment.
Bidirectional bounds stabilize training of energy-based models.
BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.
Paper proposes BMPO to optimize policies using bidirectional models.
Approximately, 50 million people in the world are affected by epilepsy. For patients, the anti-epileptic drugs are not always useful and these drugs may have undesired side effects on a patient's health. If the seizure is predicted the patients will have enough time to take preventive measures. The purpose of this work…
New method for bidirectional generative modeling using adversarial gradient estimation.
Bi-Mamba model predicts diffusion coefficients and exponents from short data.
Paper improves communication in distributed optimization, reducing worker-to-server data exchanges.
Artemis framework improves distributed learning with bidirectional compression and partial participation.
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We…
Survey of GANs and autoencoders, addressing mode collapse and likelihood issues.
Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose a bidirectional recurrent neural network based approach to extract parallel sentences from collections of multilingual texts. Our experiments with noisy parallel corpora…
Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. However, BiLSTM…
New method learns causal relationships in latent variables.
Paper analyzes and compares ELF algorithms for federated learning.
Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks. However, most existing algorithms ignore the existence of inherent data distribution and even noises. This may significantly increase the phenomenon of over-fitting and d…
We present here a new model and algorithm which performs an efficient Natural gradient descent for Multilayer Perceptrons. Natural gradient descent was originally proposed from a point of view of information geometry, and it performs the steepest descent updates on manifolds in a Riemannian space. In particular, we ext…
Study designs experiments to identify causal graph structure with cycles and latent confounders.
A bidirectional loss function improves label distribution learning and enhancement.
Understanding customer sentiments is of paramount importance in marketing strategies today. Not only will it give companies an insight as to how customers perceive their products and/or services, but it will also give them an idea on how to improve their offers. This paper attempts to understand the correlation of diff…
A new deep learning method for energy disaggregation.
A new GAN model -GAN with tunable loss function addresses gradient vanishing and mode collapse issues.
Unbalanced GANs stabilize GAN training by pre-training the generator with VAE.
GANs can approximate SDEs for large time steps.
Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples () conditioned on both latent variables () and known auxiliary information (). We propose the Bidirectional cGAN (BiCoGAN), which effectively disentangles and in the generation process and provides a…
Recently, deep architectures, such as recurrent and recursive neural networks have been successfully applied to various natural language processing tasks. Inspired by bidirectional recurrent neural networks which use representations that summarize the past and future around an instance, we propose a novel architecture …