Delayed-RNN approximates stacked and bidirectional RNNs.
problem Improving RNN expressiveness and representational capacity.
method Weight-constrained delayed-RNN, equivalent to stacked-RNNs, with partial acausality.
result Delayed-RNN can approximate stacked and bidirectional RNNs, outperforming them in some tasks.
New method estimates bidirectional causal effects in large-scale systems.
problem Estimating bidirectional causal effects in systems with mutual dependence and heteroskedasticity.
method Heteroskedasticity-based identification with online kernel learning and random Fourier features.
result Superior accuracy and stability compared to single equation and polynomial approximations.
Sharp error bounds derived for bidirectional GANs without restrictive assumptions.
problem Estimating the error of bidirectional GANs under various conditions.
method Dudley distance, neural network functions, decomposition of IPM.
result Nearly sharp bounds for bidirectional GAN estimation error.
Proposes FBFAN to defend against adversarial attacks by learning semantic features.
problem Vulnerability of deep neural networks to adversarial attacks.
method Featurized Bidirectional Generative Adversarial Networks (FBGAN) that learns semantic features and filters non-semantic perturbations.
result FBGAN effectively reconstructs adversarial data to denoised data, improving classifier performance.
Paper proposes using LSTM for LSH-based sequence alignment.
problem Sequence alignment using deep learning models.
method Deep bidirectional LSTM for feature learning and LSH-based sequence alignment.
result Higher accuracy achieved with LSTM-based model.
SPID-GAN learns bidirectional mappings in subsurface models.
problem Challenges in identifying and approximating causal structures in high-dimensional parameter spaces.
method Generative adversarial networks (GANs) for learning cross-domain mappings.
result SPID-GAN achieves satisfactory performance in identifying bidirectional state-parameter mappings.
Bidirectional attention is shown to be equivalent to a continuous bag of words model with mixture-of-experts.
problem Understanding the statistical underpinnings of bidirectional attention.
method Exploring bidirectional attention as a mixture-of-experts model and reparameterizing it.
result Bidirectional attention can be viewed as a continuous bag of words model with mixture-of-experts weights.
DBGAN learns graph node representations by balancing distribution consistency.
problem Graph representation learning overfits due to ignoring data distribution.
method DBGAN uses a structure-aware prior distribution and bidirectional adversarial learning.
result DBGAN achieves better trade-off between robustness and dimensionality.
Bidirectional sequence generation improves performance in conversational tasks.
problem Neural sequence generation typically considers only past tokens, limiting performance.
method Introduced placeholder tokens that can consider both past and future tokens in sequence generation.
result Bidirectional model outperforms competitive baselines on conversational tasks.
Paper proposes BMPO to optimize policies using bidirectional models.
problem Model-based reinforcement learning's reliance on forward model accuracy.
method Develops BMPO using both forward and backward models for policy optimization.
result BMPO outperforms state-of-the-art methods in sample efficiency and asymptotic performance.
Bidirectional learning improves neural network robustness to noise and attacks.
problem Improving neural network robustness to adversarial and static noise.
method Bidirectional learning (BL) techniques using error propagation and hybrid adversarial networks (HAN).
result Both methods improve robustness and accuracy, with HAN showing state-of-the-art performance.
Bi-Mamba model predicts diffusion coefficients and exponents from short data.
problem Characterizing anomalous diffusion in complex systems.
method Bidirectional state-space deep learning architecture.
result Efficient inference of diffusion coefficient and exponent from short trajectories.
Bidirectional VAE reduces parameters and improves image tasks.
problem Improving image reconstruction, classification, interpolation, and generation.
method Uses a single neural network for both encoding and decoding in both forward and backward directions.
result Bidirectional VAEs reduce parameters by almost 50% and slightly outperform unidirectional VAEs.
New method for bidirectional generative modeling using adversarial gradient estimation.
problem Bidirectional generative modeling with various f-divergences. method Adversarial gradient estimation for f-divergence optimization. result Similar algorithms for different f-divergences with varying scaling. Artemis framework improves distributed learning with bidirectional compression and partial participation.
problem Learning in distributed or federated settings with communication constraints and device partial participation.
method Artemis framework using bidirectional compression, memory mechanism, and Polyak-Ruppert averaging.
result Fast rates of convergence (linear up to a threshold) under weak assumptions on stochastic gradients.
New bidirectional model predicts magnetohydrodynamics fields and estimates uncertainty.
problem Predicting multiple fields in magnetohydrodynamics with uncertainty.
method Bidirectional autoregressive latent diffusion approach.
result Model can estimate uncertainty without ground truth using self-supervised consistency.
Unified approach to stabilize adversarial learning for joint distribution matching.
problem Non-identifiability issues in bidirectional adversarial training.
method Unified framework of adversarial and non-adversarial approaches, stabilizing learning.
result Stabilized learning of unsupervised and semi-supervised bidirectional adversarial methods.
BiCoGAN improves cGANs by disentangling latent and auxiliary variables.
problem Improving disentanglement of latent and auxiliary variables in cGANs.
method BiCoGAN uses bidirectional training with extrinsic factor loss and dynamically-tuned importance weight.
result BiCoGAN encodes auxiliary variables more accurately and disentangles latent and auxiliary variables effectively.
A bidirectional loss function improves label distribution learning and enhancement.
problem Challenges in label distribution learning and label enhancement.
method Bidirectional loss function to address dimensional gap and label enhancement.
result The bidirectional loss function improves the accuracy of label distribution learning and enhancement.
Improved sentence modeling using Suffix Bidirectional LSTM.
problem Sequential bias in BiLSTMs limits long-range dependencies.
method Encodes each suffix and prefix of a sequence in both forward and reverse directions.
result SuBiLSTM improves performance in various NLP tasks.
Paper analyzes and compares ELF algorithms for federated learning.
problem Improving efficiency and privacy in federated learning.
method Proposes P-ELF, D-ELF, and B-ELF algorithms with primal, dual, and bidirectional compression.
result Provides non-asymptotic convergence guarantees under Log-Sobolev inequality.
Bidirectional LSTM predicts seizures with 84% accuracy.
problem Predicting seizures for epilepsy patients to prevent drug side effects.
method Trained EEG data from canines on a double Bidirectional LSTM layer.
result AUC of 0.84 on test dataset, significantly better than SVM and GRU networks.
Paper improves video feature learning for better downstream tasks.
problem Improving video feature learning for better performance on downstream tasks.
method Self-supervised learning approach using contrastive bidirectional transformer, extending BERT for real-valued feature vectors.
result Significantly improved performance on video classification, captioning, and segmentation tasks.
A new deep learning method for energy disaggregation.
problem Energy disaggregation or non-intrusive load monitoring (NILM) to identify individual appliance power usage.
method Sequence to Point Learning based on Bidirectional Dilated Residual Network (BRDN).
result Our method outperforms state-of-the-art approaches in all appliances on REDD and UK-DALE datasets.
Bidirectional bounds stabilize training of energy-based models.
problem Training energy-based models is difficult and prone to instability.
method Propose bidirectional bounds linking to gradient penalty and Jacobi-determinant estimator.
result Significant stabilization and high-quality density estimation achieved.
Semi-supervised learning using BiGAN with triplet loss.
problem Training GANs with limited labeled data.
method BiGAN with triplet loss for semi-supervised learning.
result BiGAN latent space features improve classification and retrieval.
BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.
problem Accurate inflation forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index.
method Bi-directional Hierarchical Recurrent Neural Network (BiHRNN) model that uses bidirectional information flow between levels and informative constraints on RNN parameters.
result BiHRNN significantly outperforms traditional RNN models in forecasting accuracy.
Extracts parallel sentences for machine translation.
problem Data sparsity in multilingual natural language processing.
method Bidirectional recurrent neural network approach.
result Significant improvements in machine translation performance.
Proposes a dynamic model for urban traffic volume prediction.
problem Urban traffic volume prediction for better traffic management and driver planning.
method Combines bidirectional LSTM, attention mechanism, and external features.
result Improves prediction precision by 3-7 percent on NYC-Taxi and NYC-Bike datasets.
Bidirectional whitening improves neural network performance.
problem Improving the efficiency and effectiveness of neural networks.
method Extending whitening process to both forward and backward propagation phases.
result Bidirectional whitening enhances natural gradient descent for better performance.
Paper improves communication in distributed optimization, reducing worker-to-server data exchanges.
problem Efficiency in server-to-worker communication in distributed optimization.
method MARINA-P, a novel downlink compression method using correlated compressors; M3, combining MARINA-P with uplink compression.
result MARINA-P achieves provably superior server-to-worker communication complexity with increasing number of workers.
We propose a new cognitive framework for option price modelling, using quantum neural computation formalism. Briefly, when we apply a classical nonlinear neural-network learning to a linear quantum Schrödinger equation, as a result we get a nonlinear Schrödinger equation (NLS), performing as a quantum stochastic filter…
DRUM discovers interpretable rules from knowledge graphs for unseen entities.
problem Inductive link prediction on unseen entities and lack of interpretability.
method Differentiable approach using bidirectional RNNs for low-rank tensor approximation.
result DRUM outperforms existing methods in inductive link prediction.
Improves text-to-image generation with bidirectional capabilities.
problem Generating realistic images from text descriptions.
method Integrates text and image modalities using MMVR architecture with n-gram cost function and multiple sentences.
result Significant improvement in image quality over existing methods (over 20%).
TimeVQVAE uses VQ for better time series generation.
problem Training GANs and RNNs for time series generation have limitations.
method Vector quantization with bidirectional transformer priors in time-frequency domains.
result Generates high-quality synthetic signals with better temporal consistency.
CLS measures dataset similarity through decision rule performance.
problem Measuring dataset similarity in machine learning, especially for transfer learning and domain adaptation.
method Cross-Learning Score (CLS) measures similarity through bidirectional generalization performance of decision rules, linking to cosine similarity under canonical linear models.
result CLS effectively measures dataset similarity and transferability, validated on synthetic and real-world datasets.
Paper tackles stutter detection using deep learning.
problem Identification and classification of stuttered speech.
method Uses a deep residual network with bidirectional LSTM layers.
result Achieves an average miss rate of 10.03%, outperforming state-of-the-art.
PLUS pre-trains protein sequences with structural info, improving performance.
problem Lack of labeled protein sequences for training models.
method PLUS combines masked language modeling with same-family prediction for pre-training.
result PLUS-RNN outperforms other models in protein biology tasks.
Improved traffic forecasting model handles missing data.
problem Short-term traffic forecasting with missing values.
method Proposed SBU-LSTM architecture with bidirectional and unidirectional LSTM.
result Superior performance in accuracy and robustness for network-wide traffic prediction.
BRITS fills missing values in correlated time series data without specific assumptions.
problem Missing values in correlated time series data.
method Bidirectional Recurrent Neural Networks (RNN) for imputation without specific assumptions.
result BRITS outperforms state-of-the-art methods in imputation and classification/regression accuracies.
Bidirectional diffusion models predict their own rollout errors without ground truth.
problem Long rollouts accumulate error in autoregressive models, lacking a reliable test-time error signal.
method Train a bidirectional latent diffusion model that steps forward or backward, measuring discrepancies to estimate error.
result Bidirectional consistency Ci ranks rollout error and predicts magnitude with high accuracy. Hybrid model combines RNNs, encoders-decoders, and Transformers for sequence tasks.
problem Sequence labelling tasks
method Combination of bidirectional RNNs, encoder-decoder, and Transformer models
result Results are close to state-of-the-art and better for some tasks
This paper uses deep learning to classify different types of cracks from acoustic emission events.
problem Classifying different types of cracks from acoustic emission events.
method Combining deep neural networks with Bidirectional Long Short Term Memory and statistical analysis.
result Achieves 92% accuracy in classifying different types of cracks.
A single BLSTM network tackles ambiguous words in text data.
problem Ambiguity in text data, especially in technical domains.
method Proposes a single Bidirectional LSTM network for all ambiguous words.
result Comparable performance to top WSD algorithms on SensEval-3 benchmark.
U-Det improves lung nodule segmentation in CT images.
problem Challenging shapes and surroundings of lung nodules in CT images.
method End-to-end deep learning with Bi-FPN, Mish activation, and class weights.
result U-Det achieves 82.82% Dice similarity coefficient, comparable to human experts.
Study designs experiments to identify causal graph structure with cycles and latent confounders.
problem Identify causal graph structure with cycles and latent confounders.
method Established lower bounds, developed CI and do see tests algorithms, and proved tightness.
result Proposed algorithms can recover all causal edges except for double adjacent bidirected edges.
Sketch-BERT learns vector sketches using BERT-like self-supervised learning.
problem Lack of effective vector sketch representation for recognition and retrieval tasks.
method Generalized BERT to sketch domain with novel embedding networks and self-supervised sketch gestalt learning.
result Improved performance on sketch recognition, retrieval, and gestalt tasks.
New models for causal effect identification without directed cycles.
problem Identifying causal effects in complex graphical models.
method Introduces new graphical models with directed, undirected, and bidirected edges, without cycles. Provides algorithms for identification and learning from data.
result Developed algorithms for identifying causal effects in new models and gated models.