New neural network separates singing voices from music using cross entropy loss.
problem Separating singing voices from music accompaniment.
method Deep Convolutional Neural Network (CNN) trained with Ideal Binary Mask (IBM) and cross entropy loss.
result Proposed CNN outperforms existing systems in MIREX evaluations.
AFS-BM improves model accuracy by dynamically selecting features.
problem Feature selection challenges in ML, especially scalability and adaptability.
method Joint optimization for feature selection and model training with binary masking.
result AFS-BM achieves significant improvements in model accuracy and computational efficiency.
A new network predicts object/non-object masks to improve crowd counting.
problem Counting objects in images or videos with varying density.
method Proposes a network with a branch for object/non-object mask prediction, integrating it into the density map estimation.
result Demonstrates superior performance compared to baseline methods on five public datasets.
MARS automatically selects tensor decomposition ranks, improving performance in neural network tasks.
problem Determining optimal decomposition ranks in tensor decompositions.
method MARS uses binary masks to learn optimal tensor structure during training via relaxed MAP estimation.
result MARS achieves better results than previous methods in various tasks.
We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the pixels of the image that preserves the manifold's geometric structure present in the original data. Such masking implements a form of compressive sensing through emerging imaging sensor platforms for which the pow…
Model detects electricity theft with high accuracy.
problem Detecting electricity theft on imbalanced datasets.
method Multi-head self-attention mechanism with dilated convolutions and binary mask.
result Achieved AUC of 0.926, improving previous work by 17%.
We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a binary mask to preserve the spatial locality of the feature…
We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions. Complex symmetric alpha-stable distributions have been recently showed to better model audio signals in the time-frequency domain than classical Gaussian distributions thanks to their larger dynamic range. However, infer…
A novel method for feature selection using a reparameterized logitNormal distribution.
problem Feature selection for reconstruction in high-dimensional data.
method Introducing a reparameterization of the logitNormal distribution to address differentiability and covariance issues.
result The method provides an effective exploration scheme and efficient feature selection for reconstruction.
Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms. This may not efficiently harness the time-frequency repr…
A common strategy for sparse linear regression is to introduce regularization, which eliminates irrelevant features by letting the corresponding weights be zeros. However, regularization often shrinks the estimator for relevant features, which leads to incorrect feature selection. Motivated by the above-mentioned issue…
New criteria improve imputation model selection using MOO.
problem Selecting the best imputation model using prediction accuracy metrics.
method Introduced three modified MOO criteria based on rank transformation, energy distance, and likelihood principle.
result Demonstrated how MOO is related to missing-at-random assumption and derived statistical and computational learning theories.
The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network's links using a …
Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents. The two most frequently discussed benefits of quantization are reduced memory consumption, and a faster forward pass when implemented with efficient bitwise operations. We propose a t…
StrNN uses neural network structures to learn conditional independencies.
problem Learning conditional independencies in neural networks.
method Designing masks for neural networks based on binary matrix factorization.
result StrNN improves density estimation and causal inference.
This paper improves multichannel speech enhancement using complex ratio masking and channel-attention.
problem Limited performance of deep learning methods in multichannel speech enhancement.
method Introduces complex ratio masking and channel-attention mechanism inside a U-Net architecture.
result Demonstrates superior performance on the CHiME-3 dataset.
Sparse random networks reduce communication in federated learning.
problem Large communication cost in federated learning.
method Freeze random weights, train stochastic binary mask to sparsify.
result Improves accuracy, reduces communication, speeds convergence.
This research uses machine learning to approximate ideal and hotelling observer performance for binary signal detection.
problem Approximating the Ideal and Hotelling Observers for binary signal detection tasks.
method Supervised learning methods, including CNNs and SLNNs, are employed to approximate the IO and HO test statistics.
result The proposed supervised learning methods provide accurate approximations of the IO and HO test statistics.
New principle for disentangling latent factors using sparse regularization.
problem Disentangling latent factors from complex data.
method Sparse regularization of latent mechanisms to induce disentanglement.
result Recovery of latent variables up to permutation under certain conditions.
New pruning technique reduces index size for DNNs.
problem Irregular index form in fine-grained pruning limits parallelism and memory usage.
method Proposes a low-rank binary index matrix for efficient compression and decompression.
result Fine-grained pruning with binary matrices achieves lower memory footprint and higher parallelism.
AOFP prunes CNN filters faster and more accurately.
problem Finding optimal CNN width and pruning filters efficiently.
method Binary search, random masking, multi-path framework.
result AOFP achieves faster pruning with minimal accuracy loss.
White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used…
Advanced deep learning model improves speech enhancement by estimating phase accurately.
problem Difficulty in estimating the phase of clean speech in speech enhancement.
method Proposes Deep Complex U-Net, polar coordinate-wise complex-valued masking, and wSDR loss function.
result Achieves state-of-the-art performance in all metrics, outperforming previous approaches.
XTNet estimates complex cross-treatment effects in multi-category, multi-valued settings.
problem Challenges in estimating causal effects for multi-category, multi-valued treatments.
method Dynamic Neural Masking for capturing treatment interactions without restrictive assumptions.
result XTNet consistently outperforms state-of-the-art baselines in multi-category, multi-valued treatment effect estimation.
Paper learns causal structures from data using gradient-based optimization.
problem Learning causal structures from observational data.
method Reformulates SEM with binary adjacency matrix and uses gradient-based optimization.
result Gradient-based method efficiently identifies causal structures from data.
New method approximates Ideal Observer using GANs and MCMC.
problem Intractable computation of Ideal Observer test statistic.
method Applying MCMC to SOMs learned by GANs.
result Extends applicability of MCMC for IO performance.
This research improves transparency in RNN predictions of ICU mortality risk.
problem Lack of transparency in RNNs' healthcare predictions.
method Introduced Learned Binary Masks (LBM) and KernelSHAP for identifying EMR variables' contributions to RNN risk of mortality predictions.
result Attribution matrices show each input feature's contribution to RNN predictions, facilitating analysis of the model and its predictions.
New algorithm trains binary-activation, multi-level RNNs for noise-resilient, ADC-/DAC-free PIM inference.
problem Training noise-resilient, ADC-/DAC-free neural networks.
method Binary activations and multi-level weights for eNVM-based processing-in-memory circuits.
result Higher accuracy and noise resilience for recurrent networks compared to existing methods.
LatFormer improves geometric reasoning by incorporating lattice symmetry priors in attention mechanisms.
problem State-of-the-art models struggle with geometric reasoning tasks in the ARC and LARC datasets.
method Introduced LatFormer, a model that uses lattice symmetry priors in attention masks.
result LatFormer requires 2 orders of magnitude fewer data than standard attention mechanisms.
PMI-Masking improves MLM pretraining by masking correlated spans efficiently.
problem Uniform token masking leads to inefficient and suboptimal performance in MLMs.
method PMI-Masking uses Pointwise Mutual Information to mask n-grams with high collocation.
result PMI-Masking reaches half the training time and improves performance.
New method predicts activity coefficients for binary mixtures without using physical descriptors.
problem Predicting activity coefficients for unexplored binary mixtures.
method Probabilistic matrix factorization model.
result Method outperforms state-of-the-art models requiring less training effort.
A new loss function α-loss bridges log-loss and 0-1 loss for binary classification.
problem Improving binary classification performance using a tunable loss function.
method Introducing α-loss, proving its margin-based form and classification-calibration, and providing an upper bound on empirical risk. result Empirical and expected risk difference upper bound for logistic regression-based classification.
FMix improves model performance by distorting learned functions without distorting data distribution.
problem Improving model robustness and generalization performance in mixed sample data augmentation.
method Proposes FMix, an MSDA using random binary masks generated from Fourier space.
result FMix outperforms MixUp and CutMix, achieving state-of-the-art results on CIFAR-10.
NICE framework explains CNN predictions and compresses images.
problem Interpreting deep neural networks and efficient image compression.
method End-to-end Neural Image Compression and Explanation (NICE) framework.
result NICE achieves high compression rates while maintaining classification accuracy.
ST-MTM models complex time series by decomposing and masking seasonal and trend components.
problem Forecasting complex time series with intricate temporal variations.
method Seasonal-Trend Decomposition with Masking and Contrastive Learning.
result ST-MTM achieves superior forecasting performance compared to existing methods.
The kernel embedding algorithm is an important component for adapting kernel methods to large datasets. Since the algorithm consumes a major computation cost in the testing phase, we propose a novel teacher-learner framework of learning computation-efficient kernel embeddings from specific data. In the framework, the h…
Expands MLM by masking token positions, improving performance and convergence.
problem Improving language model performance and convergence.
method Masking token positions along with [MASK] tokens, using a fully connected classifier stage.
result Shows .3% improvement and 50% faster convergence for BERT Base with position masking.
SMART training improves mask-predict translations.
problem Closing the performance gap between semi-autoregressive and autoregressive models.
method SMART training method for conditional masked language models.
result SMART-trained models produce higher-quality translations.
Models predict probabilities of causation from limited data.
problem Estimating probabilities of causation requires unreliable or impractical experimental and observational data.
method Proposed Exact-MLP and Mask-MLP models trained on reliable subpopulations.
result Models achieve average MAEs of roughly 0.03, reducing MAE by 80%.
New method reduces diffusion model function evaluations for discrete data.
problem High computational burden in generating samples from masked diffusion models.
method Modified causal attention mask and speculative sampling mechanism for non-factorized predictions.
result Achieved ~2x reduction in required network forward passes.
Proposes a proportional masking strategy for better tabular data imputation.
problem Heterogeneity of tabular data disrupts uniform random masking in MAEs.
method Computes missingness statistics, generates proportional masks, uses MLP token mixing.
result Proportional masking preserves missingness distribution, improves imputation performance.
New analysis reveals masked self-supervised learning's effectiveness in extracting data structure.
problem Analyzing masked self-supervised learning in high-dimensional data.
method Developed precise high-dimensional analysis of masked modeling objectives.
result Identified phase transitions and structured regimes for masked self-supervised learning.
This paper connects masked pre-training to Bayesian model selection.
problem Understanding the success of masked pre-training and its generalization.
method The paper shows masked pre-training corresponds to maximizing the marginal likelihood.
result Masked pre-training with a suitable scoring function maximizes the marginal likelihood.
Optimizes pruning masks for neural networks using probabilistic fine-tuning and PAC-Bayes bounds.
problem Improving neural network performance through adaptive pruning of weights.
method Optimizes stochastic pruning masks by minimizing expected loss, considering data-adaptive regularization and feature alignment.
result Probabilistic fine-tuning leads to improved test error over baseline methods in neural networks.
New optimization methods for neural networks improve classification accuracy.
problem Optimizing shallow networks for binary classification.
method Introducing a new family of optimization problems not covered by existing methods.
result New training algorithms with stable convergence and improved performance.
Recent object detectors use four-coordinate bounding box (bbox) regression to predict object locations. Providing additional information indicating the object positions and coordinates will improve detection performance. Thus, we propose two types of masks: a bbox mask and a bounding shape (bshape) mask, to represent t…
Paper proposes a machine learning framework for VLSI mask optimization.
problem Costly VLSI mask optimization due to complex processes.
method Heterogeneous OPC framework using machine learning.
result Demonstrates efficiency and effectiveness of the proposed framework.
The paper investigates the effects of invalid action masking in policy gradient algorithms.
problem Invalid actions in policy gradient algorithms can lead to suboptimal performance.
method The paper provides theoretical justification and empirical demonstrations of the importance of invalid action masking.
result Invalid action masking is crucial as the number of invalid actions increases.