New model generates code from GUI screenshots using attention and hierarchical structure.
problem Challenges in converting abstract image features to code.
method Attention-based hierarchical decoder trained end-to-end.
result Model outperforms state-of-the-art methods on GUI-code datasets.
This work proposes optimal decision rules for hierarchical classifiers to better align with evaluation metrics.
problem Heuristic decision rules in hierarchical classification do not align with evaluation metrics.
method Derives optimal decision rules for various prediction settings, focusing on hierarchical hFβ scores. result Optimal decision rules enhance the performance and reliability of hierarchical classifiers.
As a technology to read brain states from measurable brain activities, brain decoding are widely applied in industries and medical sciences. In spite of high demands in these applications for a universal decoder that can be applied to all individuals simultaneously, large variation in brain activities across individual…
A transformer model improves spell correction with hierarchical attention.
problem Improving spell correction accuracy and speed.
method Multi encoder-single decoder transformer architecture with hierarchical attention.
result Significant improvement in CER, WER, and SER error rates.
Novel ML approach solves complex warehouse routing problem.
problem Efficiently routing pickers in mixed-shelves warehouses.
method Hierarchical and parallel multi-agent reinforcement learning.
result State-of-the-art performance in solution quality and inference speed.
New model generates larger molecules more effectively.
problem Previous graph generation techniques struggle with larger molecules.
method Hierarchical graph encoder-decoder using structural motifs.
result Model significantly outperforms previous baselines on molecule generation tasks.
Paper proposes a deep learning architecture for generating long stories from images.
problem Maintaining context in long event sequences for visual storytelling.
method Hierarchical deep learning architecture with encoder-decoder networks and natural language descriptions.
result Our method outperforms state-of-the-art techniques on automatic evaluation metrics.
Hierarchical OT improves alignment of multimodal datasets.
problem Aligning related but noisy datasets.
method Hierarchical optimal transport with clustered structure.
result Hierarchical alignment improves cross-domain data matching.
A new method for hierarchical clustering using continuous embeddings and optimization.
problem Hierarchical clustering with provable quality guarantees.
method Continuous relaxation of discrete optimization problem using hyperbolic embeddings and decoding.
result Continuous relaxation yields a discrete tree with (1 + epsilon)-factor approximation for optimal tree.
Learning in the latent variable model is challenging in the presence of the complex data structure or the intractable latent variable. Previous variational autoencoders can be low effective due to the straightforward encoder-decoder structure. In this paper, we propose a variational composite autoencoder to sidestep th…
Mixed DNN approach improves EEG-based speech imagery recognition.
problem Automatic identification of imagined speech from EEG.
method Hierarchical deep neural network strategy combining CNN, RNN, and autoencoders.
result 23.45% improvement in accuracy over baseline method.
Develops a deep model for joint image-text learning.
problem Bidirectional joint image-text modeling.
method Variational hetero-encoder randomized GAN (VHE-GAN).
result Achieves state-of-the-art performance in image-text learning and generation.
A new model learns from few examples using latent variables.
problem Learning from few examples with powerful neural networks.
method Variational Homoencoder (VHE) for hierarchical latent variable model.
result VHE outperforms existing models on test set likelihood and one-shot tasks.
Improved hierarchical discrete VAEs for better stability and performance.
problem Training stable and efficient hierarchical discrete VAEs with numerous latent variables.
method Introducing Relaxed-Responsibility Vector-Quantisation to parameterise discrete latent variables in a hierarchical structure.
result Achieved state-of-the-art bits-per-dim results for various standard datasets.
This work tackles posterior collapse in conditional and hierarchical VAEs.
problem Posterior collapse in VAEs leads to poor latent variable representations.
method Theoretical analysis of linear conditional and hierarchical VAEs, empirical validation.
result Theoretical and empirical evidence of posterior collapse causes in conditional and hierarchical VAEs.
Hierarchical autoregressive models improve image quality by learning abstract representations.
problem Local structure bias in autoregressive models leads to lack of large-scale coherence in generated images.
method Propose two methods to learn discrete representations of images that abstract away local detail, and train autoregressive priors on these representations.
result Hierarchical autoregressive models produce high-fidelity reconstructions and realistic images with large-scale coherence.
The paper tackles entity induction and reference in visual storytelling.
problem Coherence in visual storytelling through proper entity introduction and reference.
method Building an entity skeleton, using an encoder-decoder framework, and proposing a glocal hierarchical attention model.
result The proposed models outperform the baseline in automatic evaluation metrics and human preference.
CompILE learns reusable segments from demonstrations for hierarchical task execution.
problem Learning reusable, variable-length segments of hierarchical behavior from demonstrations.
method Unsupervised, fully-differentiable sequence segmentation module for latent encoding and re-composition.
result Model generalizes to longer sequences and unseen environments, learns task boundaries and event encodings.
U-Nets use belief propagation for efficient image denoising and classification.
problem Efficiently denoise and classify images using generative hierarchical models.
method Interpreted U-Nets as implementing belief propagation in tree-structured models.
result U-Nets can efficiently approximate denoising functions with sample complexity bounds.
Graph-to-Tree Neural Networks improve structured input-output translation in tasks like semantic parsing and math word problems.
problem Improving performance on tasks like semantic parsing and math word problem solving.
method Graph-to-Tree Neural Networks, consisting of a graph encoder and a hierarchical tree decoder.
result Graph2Tree model outperforms or matches state-of-the-art models on neural semantic parsing and math word problem tasks.
We discuss an autoencoder model in which the encoding and decoding functions are implemented by decision trees. We use the soft decision tree where internal nodes realize soft multivariate splits given by a gating function and the overall output is the average of all leaves weighted by the gating values on their path. …
Director learns hierarchical behaviors from pixels, outperforming exploration methods.
problem Challenges in learning long-term hierarchical behaviors from pixels.
method Directly learns hierarchical behaviors from pixels using a latent space world model.
result Director outperforms exploration methods on various tasks.
A network supporting deep unsupervised learning is presented. The network is an autoencoder with lateral shortcut connections from the encoder to decoder at each level of the hierarchy. The lateral shortcut connections allow the higher levels of the hierarchy to focus on abstract invariant features. While standard auto…
CSLVAE generates large chemical libraries efficiently.
problem Navigating ultra-large combinatorial synthesis libraries.
method Hierarchically-organized database with molecular encoder and decoder.
result Generates valid molecular graphs without autoregression.
The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-te…
SIG-VAE enhances VGAE for graph data modeling.
problem Limited flexibility in VGAE for graph data.
method Hierarchical variational framework with Bernoulli-Poisson link decoder.
result SIG-VAE outperforms state-of-the-art methods on graph tasks.
Recently, there is increasing interest and research on the interpretability of machine learning models, for example how they transform and internally represent EEG signals in Brain-Computer Interface (BCI) applications. This can help to understand the limits of the model and how it may be improved, in addition to possi…
Novel BCI system classifies imagined speech with high accuracy.
problem Classifying imagined speech from brain signals.
method Hierarchical deep learning with CNN and autoencoder.
result Achieved 83.42% average accuracy across six phonological tasks.
Solution for voice conversion with limited data using hierarchical seq2seq and attention models.
problem Voice conversion between speakers with limited parallel audio pairs.
method Hierarchical sequence to sequence model with attention-based decoder, trained on single speaker dataset.
result Improved voice conversion quality using mel spectrograms and wavenet vocoder.
Enhanced VQ-VAE generates high-fidelity images faster.
problem Generating high-fidelity images efficiently.
method Scaled VQ-VAE with fast autoregressive sampling.
result VQ-VAE generates samples with quality rivaling GANs.
New method enforces encoder sparsity in HPF for more interpretable feature selection.
problem Lack of encoder sparsity in HPF leads to lack of column-clustering property.
method Enforces encoder sparsity using a generalized additive model (GAM).
result Gains ability to perform feature selection and relates each representation to original features.
Review of autoencoder-based representation learning with meta-priors.
problem Learning useful representations with little supervision.
method Use of meta-priors and three mechanisms to enforce disentanglement and hierarchical organization.
result Autoencoder-based models can enforce disentanglement and hierarchical organization.
Develops scalable autoencoder for document networks.
problem Sparse and skewed latent node representations in document relational networks.
method Combines graph Poisson factor analysis with Weibull-based graph inference networks.
result Extracts high-quality hierarchical latent document representations.
Hierarchical MARL learns complementary skills for team coordination.
problem Creating intelligent agents that coordinate like human sports teams.
method Two-level hierarchical MARL with unsupervised skill discovery.
result Emergence of useful and complementary skills in team games.
ISVAE enhances interpretability in time series clustering using a novel filter bank.
problem Improving interpretability in time series clustering models.
method Integrates a Filter Bank (FB) into a Variational Autoencoder (VAE) to enhance interpretability and clusterability.
result ISVAE produces a more interpretable and separable encoding with enhanced clusterability.
A novel approach predicts long-term stock price trends using 2D-convolutional encoders and semantic segmentation.
problem Predicting long-term daily stock price changes with deep learning models.
method Proposes a hierarchical CNN structure with Atrous Spatial Pyramid Pooling blocks to capture both long and short-term temporal relationships.
result Achieved overall accuracy and AUC of 78.18% and 0.88 for predicting trends over the next 20 days.
Novel low-rank neural decoder improves μ-ECoG neural decoding.
problem Challenging neural decoding from high-dimensional μ-ECoG data. method Low-rank structure in neural network decoder.
result Low-rank decoder outperforms standard PCA.
The paper introduces FMCI and hybrid decoding for hidden Markov models.
problem Computing distributions and decoding hidden state sequences in HMMs.
method Finite Markov chain imbedding (FMCI) and hybrid decoding.
result Hybrid decoding improves performance over traditional methods.
Deep learning system classifies phonological categories from EEG data.
problem Speech-related BCI for people with speaking disabilities.
method Hierarchical deep learning approach using CNN, LSTM, and autoencoder.
result Average accuracy of 77.9% across five binary classification tasks.
Study examines how decoding algorithms affect fairness in language generation models.
problem Impact of decoding algorithms on fairness in open-ended language generation.
method Systematic analysis of top-p, top-k, and temperature decoding algorithms. result Decoding algorithms significantly impact fairness across demographic groups.
Improved VAEs learn flat latent spaces for better data similarity.
problem Measuring data similarity in latent spaces using Euclidean metric.
method Extend VAEs to learn flat latent manifolds using Riemannian geometry and regularisation.
result Improved performance on video-tracking benchmarks, nears supervised methods.
Paper uses RL to optimize bit-flipping decoding for binary codes.
problem Improving bit-flipping decoding for binary linear codes.
method Mapped iterative decoding algorithms to MDPs for reinforcement learning.
result Learned BF decoders offer performance-complexity trade-offs and near-optimal performance.
Enhances drug discovery by optimizing molecular structures.
problem Accelerate drug discovery through better optimization of precursor molecules.
method Integrates substructure components with atom-level encoding in a fully autoregressive graph decoder.
result Significantly outperforms previous state-of-the-art baselines on molecular optimization tasks.
A new tree-Wasserstein distance for high-dimensional data with latent feature hierarchy.
problem Finding meaningful distances between high-dimensional data samples with latent feature hierarchy.
method Proposes a new tree-Wasserstein distance (TWD) for high-dimensional data with a latent feature hierarchy, using diffusion geometry and tree decoding.
result The proposed TWD effectively recovers the latent feature hierarchy and is efficient and scalable.
Deep invertible networks decode EEG signals better than chance.
problem Decoding brain signals from EEG data.
method Deep invertible networks for generating and classifying brain signals.
result Deep invertible networks generate realistic EEG signals and classify novel signals above chance.
RL-VAE uses RL to decode molecular graphs from latent embeddings.
problem Efficiently decoding molecular graphs from latent embeddings.
method Repurposed simple graph generator for efficient decoding.
result Decoding molecular graphs from latent embeddings is possible with a simple graph generator.
Deep learning aids ADMM-based decoding for binary linear codes.
problem Improving decoding efficiency for binary linear codes.
method Designing a decoding network based on ADMM and deep learning.
result Numerical results show improved performance compared to original ADMM.
This paper analyzes speculative decoding, a method to speed up large language model inferences.
problem Theoretical understanding of speculative decoding is lacking.
method Conceptualizes speculative decoding as a markov chain problem and studies its key properties.
result Reveals fundamental connections between LLM components and their impact on decoding efficiency.