This work prevents variational autoencoders from collapsing by adding an auxiliary decoder.
problem Variational autoencoders can collapse into autodecoders, losing semantic information.
method Adding an auxiliary decoder to regularize the latent space.
result Auxiliary decoders increase semantic information in the latent space and reconstructions.
JEPA fails to improve language model performance when fine-tuning.
problem Improving language model performance through latent representation learning.
method Testing various training-time auxiliaries on natural-language-to-regex generation.
result No auxiliary improves cross-entropy gradient cosine with decoder visibility.
This paper proposes a non-parallel many-to-many voice conversion (VC) method using a variant of the conditional variational autoencoder (VAE) called an auxiliary classifier VAE (ACVAE). The proposed method has three key features. First, it adopts fully convolutional architectures to construct the encoder and decoder ne…
Improved unlearning in LLMs using contrastive decoding.
problem Removing specific information from LLMs while preserving overall performance.
method Inference-time unlearning algorithm using contrastive decoding with two auxiliary models.
result Notable gains in forget quality and retained performance compared to prior approaches.
As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer …
Topic-aware chatbot learns from NMF topic vectors.
problem Improving chatbot relevance based on user topics.
method Combines RNN with NMF for topic learning and attention.
result Chatbot provides more relevant answers based on topic.
VAIOM models financial returns using continuous input and categorical output.
problem Modeling continuous, noisy, and heterogeneous financial data.
method VAIOM is a decoder-only Transformer that separates input representation from output likelihood.
result VAIOM models outperform fixed single-bar LightGBM baseline in both Test halves.
Study improves interpretability in generative models by disentangling latent variables in scientific datasets.
problem Extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings.
method Introducing Aux-VAE, a novel architecture within the VAE framework, which disentangles latent variables by guiding them with auxiliary variables.
result Aux-VAE achieves disentanglement with minimal modifications to the standard VAE loss function, validated on multiple datasets.
Paper extends ICA to ISA with auxiliary variables for better speech representation learning.
problem Learning unsupervised speech representations with independent subspaces.
method Theoretical framework of nonlinear ISA with auxiliary variables.
result Proposes an algorithm to learn speech representations with independent subspaces.
Brain2Char decodes text from brain recordings, achieving state-of-the-art performance.
problem Directly decoding text from brain recordings for communication.
method Combines 3D Inception layers, bidirectional recurrent layers, and language model weighted beam search. Uses CTC loss and auxiliary losses for regularization.
result Achieves 10.6%, 8.5%, and 7.0% WER on vocabulary sizes from 1200 to 1900 words.
Improved text generation with constraints using discrete auto-regressive biasing.
problem Balancing fluency and constraint satisfaction in LLM outputs.
method Discrete Auto-regressive Biasing, leveraging gradients in discrete text space.
result Significantly improved constraint satisfaction with comparable fluency.
GSI improves efficiency of large language model inference.
problem Efficiently guiding test-time alignment in large language models.
method Combines soft best-of-n scaling with a reward model and speculative samples. result Achieves higher accuracy and reduced latency compared to standard methods.
Humans are able to imagine a person's voice from the person's appearance and imagine the person's appearance from his/her voice. In this paper, we make the first attempt to develop a method that can convert speech into a voice that matches an input face image and generate a face image that matches the voice of the inpu…
Paper improves text-to-SQL models with schema-aware denoising.
problem Text-to-SQL models struggle with schema linking and grammar correctness.
method Adapts transformer-based seq-to-seq model with SeaD denoising objectives and clause-sensitive decoding.
result Improves seq-to-seq model performance on WikiSQL benchmark.
Proves identifiability of deep latent variable models without auxiliary information.
problem Identify deep generative models without side information.
method Analyzes a broad class of deep latent variable models with universal approximation capabilities.
result Identifiability of generative models without side information u. Unified principle LZN unifies generative modeling, representation learning, and classification.
problem Disjoint solutions for generative modeling, representation learning, and classification.
method LZN creates a shared latent space for all tasks, using encoders and decoders for each data type.
result LZN improves FID on CIFAR10 and outperforms existing methods in representation learning and joint generation/classification.
This study rethinks the latent space in generative modeling, improving performance with less complex models.
problem Determining the optimal latent space for generative models and understanding its impact on model complexity.
method Proposed a new distance metric between latent and data distributions, and a two-stage training strategy called Decoupled Autoencoder (DAE).
result Improves generative performance with less complex models, as shown by comprehensive experiments on various models.
DAPDAG learns DAG structure to adapt predictions across domains.
problem Predicting in a new domain without labeled data.
method Auto-encoder with DAG reconstruction as auxiliary task.
result Reconstructing DAG benefits approximate inference and prediction.
Many efforts have been devoted to training generative latent variable models with autoregressive decoders, such as recurrent neural networks (RNN). Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech. We unify successful ideas from recently pr…
HAMD optimizes cubic portfolios without quadratization, achieving better results.
problem Optimizing higher-order portfolio models with reduced distortion.
method Hybrid pipeline combining continuous Hamiltonian search, cardinality-preserving projection, and iterated local search.
result HAMD achieves significantly lower native cubic objective values than classical heuristics.
Enhanced transformer converts whispered speech to natural speech.
problem Machine recognition of whispered speech is challenging.
method Proposes an enhanced transformer architecture trained end-to-end using supervised learning.
result Similar formant distributions of converted speech to groundtruth.
Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn discrete representations of images which abstract away local detail. We show th…
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approac…
AuxiLearn combines auxiliary tasks into a single loss function.
problem Improving neural network performance on a main task using auxiliary tasks.
method Implicit differentiation to learn a network that combines auxiliary tasks into a single coherent objective function.
result AuxiLearn consistently outperforms competing methods in various tasks and domains.
New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.
problem Reducing regret in parameterized bandits with correlated auxiliary feedback.
method Develops a reward estimator using auxiliary feedback with tight confidence bounds.
result Shows significant reduction in regret compared to standard methods.
In-N-Out improves model robustness to out-of-distribution data.
problem Learning robust models with few in-distribution labeled examples.
method Pre-training with auxiliary information and self-training with pseudolabels.
result In-N-Out outperforms auxiliary inputs or outputs alone on both in-distribution and OOD error.
Aux-NAS uses auxiliary labels to improve primary task performance without extra inference cost.
problem Improving primary task performance using auxiliary labels without increasing inference cost.
method Architecture-based approach with a flexible asymmetric structure for primary and auxiliary tasks, using Neural Architecture Search (NAS) to evolve networks with only primary-to-auxiliary connections.
result Achieves improved performance on multiple tasks without increasing inference cost.
ATS2S model predicts RUL of industrial equipment using attention mechanism.
problem Accurate estimation of RUL for industrial equipment to improve maintenance schedules and reduce costs.
method ATS2S model that optimizes reconstruction and RUL prediction losses, uses attention mechanism, and integrates encoder and decoder features.
result ATS2S model achieves superior performance over 13 state-of-the-art methods on four real datasets.
Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiri…
Researchers identify valid auxiliary functions for extreme value distributions and their max-domains of attraction.
problem Characterize valid auxiliary functions for extreme value distributions and their max-domains of attraction.
method Introduced 'universal' auxiliary functions valid for both VR and vMR representations, identified sets of valid auxiliary functions, and proposed a method for finding appropriate auxiliary functions.
result Characterized valid auxiliary functions for both VR and vMR representations for the entire MDA distribution families.
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…
Auxiliary Tuning adapts pre-trained models for novel tasks efficiently.
problem Adapting pre-trained models for new tasks efficiently.
method Supplementing pre-trained model with an auxiliary model that shifts output distribution.
result Achieved similar results to training from scratch with fewer resources.
The paper improves high-dimensional linear regression prediction and estimation using auxiliary samples.
problem Estimating and predicting high-dimensional linear regression models with auxiliary samples.
method Proposes Trans-Lasso for data-driven transfer learning, establishing optimality for prediction and estimation.
result Knowledge from auxiliary samples can improve learning performance in target problems.
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.
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.
Method reweights auxiliary tasks to reduce data need for main task.
problem Limited labeled data for supervised learning.
method Formulates weighted likelihood function as surrogate prior, minimizing divergence to true prior.
result Effective use of limited labeled data with auxiliary tasks, improving performance.
This paper reviews dMTL and methods for selecting auxiliary tasks.
problem Improving model performance for multiple tasks.
method Review of dMTL approaches and methods for selecting auxiliary tasks.
result Methods for selecting auxiliary tasks can improve dMTL performance.
Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables. We consider the setting of incomplete data analysis, where some primary variables are not observed. Utilizing a parametric model of joint distribution of prima…
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.
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.
MFAI uses gradient boosted trees to leverage auxiliary info for scalable Bayesian matrix factorization.
problem Matrix factorization struggles with poor data quality, especially high sparsity and low SNR.
method Integrates gradient boosted trees into probabilistic matrix factorization framework.
result MFAI effectively leverages auxiliary information, improving model performance.
Multi-task learning (MTL) has achieved success over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary tasks. However, when the usefulness of the auxiliary tasks w.r.t. the primary task is not known a priori, the success of MTL models depends on th…
Diffusion models improve image compression at low bit-rates.
problem Efficiently compressing images at very low bit-rates.
method Encoding into an embedding, using diffusion models to refine the embedding iteratively.
result Realistic reconstructions can be generated at extremely low bit-rates.
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.
Proves Bochner's identity on graphs using a new auxiliary graph.
problem Extending Bochner's identity to graph theory.
method Introduces a complete tangent graph to prove the identity.
result Validates Bochner's identity on graphs.