arXiv research

New method identifies valid IVs for bi-directional MR with invalid instruments.

Improved Q&A model with LSTM and bi-directional attention.

BICompFL tackles bi-directional compression challenges in stochastic FL, reducing communication costs by an order of magnitude.

Proposes a new Helmholtz machine model outperforming previous models.

New risk class penalizes loss deviations from mean on both sides.

DoCoFL compresses model updates for cross-device federated learning.

We discuss two parameterizations of models for marginal independencies for discrete distributions which are representable by bi-directed graph models, under the global Markov property. Such models are useful data analytic tools especially if used in combination with other graphical models. The first parameterization, i…

New LSTM structure improves text representation efficiency.

Bi-directional Curriculum Learning improves graph anomaly detection by considering both homogeneity and heterogeneity.

This work compares NN architectures for spectrum sensing.

BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.

FedSGM tackles constrained federated learning with unified framework.

I-BERT extends Transformer's self-attention to arbitrary input lengths.

Graphical models with bi-directed edges (<->) represent marginal independence: the absence of an edge between two vertices indicates that the corresponding variables are marginally independent. In this paper, we consider maximum likelihood estimation in the case of continuous variables with a Gaussian joint distributio…

Improved NER performance on imbalanced data.

Novel technique detects adversarial samples in face recognition models.

JMVAE fails to generate large modalities, JMVAE-kl and JMVAE-h solve this issue.

The covariance graph (aka bi-directed graph) of a probability distribution $p$ is the undirected graph $G$ where two nodes are adjacent iff their corresponding random variables are marginally dependent in $p$. In this paper, we present a graphical criterion for reading dependencies from $G$, under the assumption that $…

Study neural networks learning from noisy examples via reverberation.

We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes. The alignment between the input and output sequences is established usin…

AER combines auto-encoder and LSTM for better time series anomaly detection.

PHI Scrubber removes sensitive patient info from medical notes.

A new method predicts protein functions using variable-length sequences.

Project classifies Hinglish social content on platforms like Twitter, Reddit.

Proposes a stochastic model for South African actuarial use.

Bi-LSTM with attention generates jazz music with rich nuances.

Bi-GAN model for imputing and predicting irregular time-series data.

Improved sentiment analysis explanations using LRP for RNNs.

VBTA learns across domains using triplet information.

Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and …

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by usi…

Neural model parses non-projective dependency trees efficiently.

Knots can be constructed and decomposed using Murasugi sums of Seifert surfaces.

System builds Somali ASR for UN humanitarian efforts.

New framework using Jensen-Shannon divergence improves domain adaptation theory.

ALAD uses GANs to detect anomalies in complex data.

PML-GAN tackles noisy multi-label annotations using adversarial learning.

CCHM algorithm learns BN structure with latent variables, improving causal effect measurement.

A comparison of SLDS and LSTM for pedestrian behavior prediction shows SLDS works better with shorter sequences.

BiSHop tackles tabular data challenges with sparse Hopfield layers.

Several methods exist to infer causal networks from massive volumes of observational data. However, almost all existing methods require a considerable length of time series data to capture cause and effect relationships. In contrast, memory-less transition networks or Markov Chain data, which refers to one-step transit…

Framework generates pop song melodies and piano accompaniment.

Paper proposes RCD method to discover causal structure with latent confounders.

WeSinger improves singing voice synthesis with data augmentation and specialized modules.

Smooth flows for physical systems with smooth energies and forces.

SAPS synthesizes code from natural language specifications.

A new method for identifying Gaussian process state space models.

Deep learning predicts patient trajectories in open Mimic-III dataset.