CTCModel extends Keras for transparent CTC classification.
problem Handling unsegmented input sequences with labels related to subsets of frames.
method Combines Keras and CTC implementation in Tensorflow backend.
result CTCModel predicts sequences of labels from unsegmented input.
This work argues for the importance of objects in model-based reinforcement learning.
problem Dynamic construction of models in real-world problems.
method Identifies requirements for overcoming limitations in connectionist models.
result Objects are essential for dynamic model construction in reinforcement learning.
Analogator learns to make analogies by example.
problem Developing a computer program to learn analogies.
method Connectionist approach using a recurrent network architecture trained to divide scenes into figure and ground.
result Analogator can make new analogies between novel situations.
We propose a novel family of connectionist models based on kernel machines and consider the problem of learning layer-by-layer a compositional hypothesis class, i.e., a feedforward, multilayer architecture, in a supervised setting. In terms of the models, we present a principled method to "kernelize" (partly or complet…
Article evaluates AI security threats and proposes multiple measures.
problem Threats to AI integrity and security.
method Literature review, analysis of AI supply chain, discussion of mitigations.
result Multiple protective measures are necessary for AI security.
Improved speech recognition using EEG and video.
problem Enhancing continuous speech recognition systems.
method Implemented a CTC-based ASR model using EEG features.
result EEG features improve continuous visual speech recognition.
End-to-end speech recognition using EEG without speech input.
problem Speech recognition without direct speech input.
method Implemented attention model and CTC-based ASR systems for EEG signals; fused EEG with noisy speech features.
result Demonstrated end-to-end speech recognition using EEG signals.
Enhances neural networks with logical knowledge for better performance.
problem Improving neural network performance with logical knowledge.
method Integrating logical knowledge into neural networks through a new final layer with learnable clause weights.
result KENN outperforms other methods in collective classification tasks with relational data.
Continuous speech recognition from brain activity without vocalization.
problem Recognizing silent speech from EEG signals.
method Implemented a CTC ASR model using EEG signals.
result Demonstrated feasibility of EEG for continuous silent speech recognition.
One conjecture in both deep learning and classical connectionist viewpoint is that the biological brain implements certain kinds of deep networks as its back-end. However, to our knowledge, a detailed correspondence has not yet been set up, which is important if we want to bridge between neuroscience and machine learni…
Planar neural networks learn image transformations from sequences.
problem Learning image transformations for mental simulation.
method Using planar neural networks, the study investigates various factors affecting the learning of image transformations.
result The approach can effectively learn and transfer image transformations, including translation, rotation, and scaling.
Paper improves EEG-based speech recognition using CTC and beam search.
problem Improving continuous speech recognition from EEG signals.
method Implemented CTC-based ASR system, initialized weights, used external language model, studied articulatory feature prediction.
result Enhanced performance of EEG-based speech recognition systems.
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic m…
This paper explores BDL hyperparameters for robust polynomial mapping with noise.
problem Designing BDL hyperparameters for robust function mapping with uncertainty quantification.
method Mapping Bayesian connectionist representations to polynomials of varying orders and noise types.
result Optimal network depth and ensemble size for prediction and uncertainty quantification.
Paper shows continuous speech recognition with EEG features, no speech input.
problem Continuous speech recognition with limited vocabulary and noisy/no speech input.
method Connectionist temporal classification (CTC) model, EEG features, new deep learning architecture.
result Continuous speech recognition achieved on limited vocabulary with noisy/no speech input.
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this lim…
Connectionist temporal classification (CTC) is widely used for maximum likelihood learning in end-to-end speech recognition models. However, there is usually a disparity between the negative maximum likelihood and the performance metric used in speech recognition, e.g., word error rate (WER). This results in a mismatch…
This paper improves ASR performance by aligning frames more accurately.
problem Disagreement between teacher-student models in frame-level alignment.
method Introduces self-knowledge distillation (SKD) to guide frame-level alignment.
result Improves both resource efficiency and performance in ASR.
Study on reducing forgetting in neural networks using compression theory.
problem Catastrophic forgetting in neural networks.
method Defined forgetting as increased description lengths, compared variational posterior approaches to prequential coding methods.
result Proposed a new continual learning method combining ML plug-in and Bayesian mixture codes.
Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models (HMMs/GMMs) have achieved the …
Multidimensional recurrent neural networks (MDRNNs) have shown a remarkable performance in the area of speech and handwriting recognition. The performance of an MDRNN is improved by further increasing its depth, and the difficulty of learning the deeper network is overcome by using Hessian-free (HF) optimization. Given…
Secure neural network inference on untrusted platforms using holographic reduced representations.
problem Secure neural network inference on untrusted platforms.
method Connectionist Symbolic Pseudo Secrets using Holographic Reduced Representations (HRR).
result Empirical robustness to attack under various threat models.
State-level minimum Bayes risk (sMBR) training has become the de facto standard for sequence-level training of speech recognition acoustic models. It has an elegant formulation using the expectation semiring, and gives large improvements in word error rate (WER) over models trained solely using cross-entropy (CE) or co…
CAT is a new ASR toolkit using CRF and CTC for state-of-the-art speech recognition.
problem Improving automatic speech recognition systems.
method CRF-based discriminative training with CTC-inspired state topology.
result CAT achieves state-of-the-art results with fewer parameters and is competitive with hybrid models.
Plug-in method decomposes latent representations into interpretable factors.
problem Decomposing latent representations in neural networks without altering the original models.
method Factors' Decomposer-Entangler Network (FDEN) that learns to decompose latent representations into mutually independent factors.
result FDEN framework effectively decomposes latent representations into interpretable factors, maintaining original model integrity.
We study a multiclass multiple instance learning (MIL) problem where the labels only suggest whether any instance of a class exists or does not exist in a training sample or example. No further information, e.g., the number of instances of each class, relative locations or orders of all instances in a training sample, …
Recently, the connectionist temporal classification (CTC) model coupled with recurrent (RNN) or convolutional neural networks (CNN), made it easier to train speech recognition systems in an end-to-end fashion. However in real-valued models, time frame components such as mel-filter-bank energies and the cepstral coeffic…
A new neural network approach reduces tracking error in index replication.
problem Efficiently replicating an index with cardinality constraints.
method Reparametrisation and stochastic neural networks for optimisation.
result Our model achieves the lowest tracking error compared to benchmarks.
User-specific KWS system learns new keywords on-device.
problem Out-of-vocabulary problem in traditional KWS systems.
method Query-by-example enrollment and testing, using phonetic posteriors and FST.
result Promising performance on two keywords, preserving simplicity.
Develops STC for sequential data with missing labels.
problem Learning from partially labeled and unsegmented sequential data.
method Introduces Star Temporal Classification (STC) using a star token and GTN framework.
result Recover most of supervised baseline performance with up to 70% missing labels.
Neural network generates music scores directly from polyphonic audio.
problem Transcribing music scores directly from polyphonic audio.
method Convolutional Recurrent Neural Network (CRNN) with CTC loss function.
result Model can learn to transcribe scores directly from audio signals.
Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the fiel…
Study combines speaker verification and voice trigger detection in a single network.
problem Separate training for speaker verification and voice trigger detection.
method Multi-task learning with a single network trained on both tasks.
result Single network achieves comparable accuracy to independent models for each task.
Recent work on end-to-end automatic speech recognition (ASR) has shown that the connectionist temporal classification (CTC) loss can be used to convert acoustics to phone or character sequences. Such systems are used with a dictionary and separately-trained Language Model (LM) to produce word sequences. However, they a…
A new framework improves ASR alignment accuracy via optimal transport.
problem Peaky behavior and alignment inaccuracies in E2E ASR models.
method Differentiable alignment framework based on 1D optimal transport.
result Significant improvement in ASR alignment performance compared to CTC.
Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.
problem Slow convergence in neural-symbolic learning due to error propagation issues.
method Introduces grammar model as symbolic prior and back-search algorithm for efficient error propagation.
result Significantly outperforms RL methods in performance, converging speed, and data efficiency.
New neural network avoids forgetting old knowledge as new data comes in.
problem Catastrophic forgetting in lifelong learning systems.
method Sequential Neural Coding Network, biologically-plausible synapse adaptation, task-executive control mimicry.
result Significantly less forgetting compared to standard neural models in experiments.
A study on preventing catastrophic forgetting in neural networks using conditional computation.
problem Catastrophic forgetting in connectionist neural networks.
method Conditional computation framework where parameters are conditioned on each input example.
result Proposed conditional rehearsal to prevent forgetting of previously learned examples.
Study shows adversarial attacks can fool speech-to-text models, and PCA is ineffective as a defense.
problem Adversarial attacks can mislead speech-to-text neural networks.
method Crafted adversarial waveforms, used PCA for defense, tested under black-box setting.
result PCA is ineffective as a defense mechanism against adversarial attacks in audio domain.
Study shows challenges in converting RNNs to FSMs due to computational complexity.
problem Understanding the equivalence and distance between RNNs and FSMs.
method Computational proofs for equivalence and distance problems between RNNs and FSMs.
result Undecidability and hardness of approximation problems between RNNs and FSMs.
Deep learning models match traditional surrogate models in accuracy and speed for satellite remote sensing.
problem Limited computational power hinders high-resolution numerical model simulations.
method Deep learning framework applied to satellite remote sensing data.
result Deep learning models can accurately and efficiently emulate numerical models.
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.
Bayesian CNN with Variational Inference improves uncertainty handling in neural networks.
problem Uncertainty in neural networks, especially in regions with little data.
method Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference.
result BayesCNN achieves equivalent performance to frequentist inference and incorporates uncertainty measurements.
In this paper we deal with the offline handwriting text recognition (HTR) problem with reduced training datasets. Recent HTR solutions based on artificial neural networks exhibit remarkable solutions in referenced databases. These deep learning neural networks are composed of both convolutional (CNN) and long short-ter…
Study develops time-continuous models and probabilistic descriptions for agent-based economic market models.
problem Formulating and describing agent-based economic market models in a time-continuous and probabilistic manner.
method Derived time-continuous formulations, discussed impact of time-scaling, proved stability, presented probabilistic descriptions using kinetic theory.
result Time-continuous formulations and probabilistic descriptions for agent-based economic market models.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.