A hybrid ASR system using conformer architecture improves word-error-rate and training speed.
problem Improving word-error-rate and training efficiency for hybrid ASR systems.
method Used conformer architecture, applied time downsampling, and transposed convolutions.
result Conformer-based hybrid model achieves competitive results and significantly outperforms BLSTM-based hybrid model.
Expert augmentation improves hybrid model generalization.
problem Limited generalization of hybrid models outside training distribution.
method Introducing expert augmentation to improve hybrid model performance.
result Expert augmentation improves generalization of hybrid models.
Hybrid models forecast EPEC energy spot prices.
problem Forecasting energy spot prices in EPEC markets.
method Combining Naive, Fourier, ARMA/GARCH, mean-reversion, jump-diffusion, and RNN models.
result Improved accuracy in forecasting compared to individual models.
Hybrid model improves weather forecasting accuracy.
problem Combining physics and machine learning for better weather predictions.
method Offline and online training of a neural network to correct model errors in the IFS.
result The hybrid model reduces forecast errors and improves accuracy.
We introduce a new framework for training deep generative models for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input x a…
Hybrid models combine interpretable and complex models for better performance and control.
problem Improving model performance and user transparency in machine learning.
method Investigates hybrid models from theory, taxonomy, and methodological perspectives.
result Hybrid models can outperform standalone black boxes and provide precise control over transparency.
Regularization is an important component of predictive model building. The hybrid bootstrap is a regularization technique that functions similarly to dropout except that features are resampled from other training points rather than replaced with zeros. We show that the hybrid bootstrap offers superior performance to dr…
A hybrid method improves Convolutional Neural Networks training.
problem Training Convolutional Neural Networks efficiently and avoiding local minima.
method Combines backpropagation with evolutionary strategies.
result Improves accuracy by 0.61% on CIFAR-10 image classification.
New method sparsifies hybrid neural ODEs for better performance and stability.
problem Excessive latent states and interactions from mechanistic models lead to training inefficiency and over-fitting.
method Automatic state selection and structure optimization combining domain-informed graph modifications with data-driven regularization.
result Improved predictive performance and robustness with desired sparsity.
Hybrid regularization avoids double descent in random feature models.
problem Avoiding the double descent phenomenon in random feature models.
method Combines early stopping and weight decay, using GCV for hyperparameter selection.
result Hybrid method successfully avoids double descent and achieves comparable generalization.
Meta learning optimizes neural network quantization for efficient inference.
problem Uniform bitwidth quantization is sub-optimal for neural network compression.
method Meta learning to automatically generate hybrid quantization policies.
result Meta learning outperforms uniform quantization and RL approaches.
Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-the-art classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better p…
Synthetic images rendered by graphics engines are a promising source for training deep networks. However, it is challenging to ensure that they can help train a network to perform well on real images, because a graphics-based generation pipeline requires numerous design decisions such as the selection of 3D shapes and …
Hybrid models improve groundwater level prediction and uncertainty analysis.
problem Predicting and analyzing uncertainty of monthly groundwater levels.
method Six evolutionary optimization algorithms (GOA, CSO, WA, GA, KA, PSO) hybridized with ANFIS, ANN, and SVM.
result ANFIS-GOA outperformed other models in predicting groundwater levels.
In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-acto…
Transformer with self-attention has achieved great success in the area of nature language processing. Recently, there have been a few studies on transformer for end-to-end speech recognition, while its application for hybrid acoustic model is still very limited. In this paper, we revisit the transformer-based hybrid ac…
We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. Com…
Deploying deep learning (DL) models across multiple compute devices to train large and complex models continues to grow in importance because of the demand for faster and more frequent training. Data parallelism (DP) is the most widely used parallelization strategy, but as the number of devices in data parallel trainin…
A hybrid training method reduces SNN training time and complexity.
problem Training deep SNNs is computationally expensive and time-consuming.
method Hybrid training technique combining initialization from converted SNNs and incremental spike-timing dependent backpropagation (STDB).
result The method converges in less than 20 epochs, reducing training complexity and time.
Novel hybrid modeling combines ML and physics for real-time diagnosis.
problem Real-time diagnosis of complex systems.
method Combines machine learning and physics-based models to create reduced-order models.
result Generated models are two orders of magnitude simpler, improving efficiency.
A hybrid ML method improves ship response predictions across different sea conditions.
problem Improving accuracy and generalizability of ML methods for ship response predictions.
method A hybrid machine learning method that corrects forces in a low-fidelity equation of motion.
result The hybrid method offers improved prediction accuracy and generalizability compared to benchmarks.
Approximate multipliers boost CNN training speed, power, and area at slight accuracy cost.
problem Improving CNN training performance in terms of speed, power, and area.
method Simulation of approximate multipliers' impact on CNN training, hybrid training method combining approximate and exact multipliers.
result Using approximate multipliers for most of training enhances speed, power, and area with minimal accuracy loss.
Quantum hybrid vision transformers improve event classification in high energy physics.
problem Excessive computational resources for training and deploying vision transformer models.
method Constructed quantum hybrid vision transformers for high energy physics event classification.
result Quantum hybrid models achieve comparable performance to classical models with fewer parameters.
A hybrid K-NN and SVM technique improves classification accuracy.
problem Improving classification accuracy in pattern recognition.
method Discriminative nearest neighbour classification combined with SVM.
result The hybrid technique outperforms state-of-the-art methods.
HDP-VFL hybridizes DP for VFL, reducing privacy costs.
problem Privacy-preserving collaborative learning from vertically partitioned data.
method Hybrid DP framework combining HE and MPC for VFL.
result Achieves DP and JDP with negligible training time and accuracy trade-offs.
Improves deep neural network training and accuracy with adaptive basis approach.
problem Gap between theoretical and practical performance of deep neural networks.
method Adaptive basis viewpoint, novel initializations, hybrid optimizer.
result Dramatic increases in accuracy and convergence rate for various DNN applications.
Hybrid RL algorithm combines offline and online data for robust and efficient policy learning.
problem Combining robust on-policy methods with efficient offline data for hybrid RL.
method Integrates off-policy training on offline data into on-policy NPG framework.
result Achieves state-of-the-art theoretical guarantees and maintains on-policy NPG guarantees.
A hybrid model combines machine learning with a land surface model to improve soil moisture predictions.
problem Improving soil moisture predictions in climatological situations.
method Noah land-surface model integrated with Gaussian Processes, using autoregressive model for out-of-sample results.
result 3-fold reduction in RMSE using one-year leave-one-out cross-validation.
CAT toolkit combines hybrid and E2E approaches for efficient speech recognition.
problem Improving speech recognition efficiency and latency.
method CTC-CRF based framework with contextualized soft forgetting.
result CAT achieves state-of-the-art results with simpler training and streaming ASR.
In this paper, we propose and investigate a variety of distributed deep learning strategies for automatic speech recognition (ASR) and evaluate them with a state-of-the-art Long short-term memory (LSTM) acoustic model on the 2000-hour Switchboard (SWB2000), which is one of the most widely used datasets for ASR performa…
Transfer learning adapted for hybrid classical-quantum neural networks.
problem Optimizing data preprocessing and feature embedding for quantum processors.
method Adapting transfer learning to hybrid networks, using a pre-trained classical network augmented by a quantum circuit.
result Demonstrated the effectiveness of quantum transfer learning for image recognition and quantum state classification.
Quantum neural tangent kernels help understand variational quantum circuits in machine learning.
problem Designing and predicting performance of variational quantum circuits.
method Using quantum neural tangent kernels and dynamical equations for loss functions.
result Analytical solutions for training dynamics in variational quantum circuits.
Hybrid approach combines user feedback and machine learning for predicting user satisfaction.
problem Measuring user satisfaction in large-scale conversational agent systems.
method Fusion of explicit user feedback and predictions from two machine-learned models trained on different data types.
result Hybrid approach significantly improves user satisfaction predictions.
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However, to the best of our knowledge, no previous work has ever succeeded in applying DRL to multi-agent problems with discrete-continuous hybrid (…
Hybrid LSTM-GNN model improves stock price prediction accuracy.
problem Enhancing stock price prediction accuracy using complex data.
method Combining LSTM for time series and GNN for graph-based analysis.
result Hybrid model reduces MSE by 10.6% compared to standalone LSTM.
Paper proposes DCT for efficient hybrid parallel training of large recommendation models.
problem Training large recommendation models at scale with efficient communication.
method Dynamic Communication Thresholding (DCT) for both Data Parallelism and Model Parallelism.
result Reduces communication by 100x and 20x during DP and MP, respectively, improving training time by 37%.
New methods improve integration of external LMs with AED models.
problem Improving performance of AED models by integrating external LMs.
method Comparing and proposing novel methods to estimate implicit LM from AED models.
result Proposed methods outperform previous approaches.
As deep neural networks continue to revolutionize various application domains, there is increasing interest in making these powerful models more understandable and interpretable, and narrowing down the causes of good and bad predictions. We focus on recurrent neural networks (RNNs), state of the art models in speech re…
VQC-MLPNet combines quantum and classical elements for scalable quantum machine learning.
problem Challenges in expressivity, trainability, and noise resilience of VQCs.
method Hybrid architecture with a VQC generating weights for a classical MLP during training.
result Improved expressivity, trainability, and robustness compared to standalone quantum or hybrid approaches.
There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence. In this work, we show for the first time t…
Predicting firm's failure is one of the most interesting subjects for investors and decision makers. In this paper, a bankruptcy prediction model is proposed based on Artificial Neural networks (ANN). Taking into consideration that the choice of variables to discriminate between bankrupt and non-bankrupt firms influenc…
Hybrid approach combines Markowitz's theory with reinforcement learning for optimal portfolio management.
problem Optimizing investment portfolios while balancing returns and risks.
method Knowledge distillation for training reinforcement learning agents.
result Achieves highest yield and Sharpe ratio of 2.03, ensuring top profitability with low risk.
This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine learning (ML), namely federated learning (FL), which trains an ML model using the rich data and computational resources of mobile clients with…
Hybrid model improves sequential data prediction by combining neural and time series models.
problem Nonlinear prediction in online settings with domain-specific feature engineering issues.
method Joint optimization of LSTM for feature extraction and SARIMAX for time series data using state space representations.
result Significant improvements in real-life competition datasets.
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
Paper proposes hybrid modeling to improve surrogate accuracy using multiple data sources.
problem Improving surrogate model accuracy by integrating simulation and real-world data.
method Two novel probabilistic approaches: separate and combined surrogates with weighting strategy.
result Hybrid models improve predictive accuracy and coverage compared to single-source surrogates.
A hybrid Convolutional VAE predicts crypto volatility surfaces, outperforming single-symbol approaches.
problem Predicting crypto volatility surfaces
method Convolutional VAE with hybrid predictor
result Model achieves 0.94-1.56 vol-point RMSE across BTC and ETH markets
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…