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Found 29 papers, 21 papers with code
Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications
The framework was created with the original motivation of resolving challenges faced when analyzing and processing data from the physical economy, including streams of data generated by IoT and enterprise systems.
Efficient Transformers with Dynamic Token Pooling
Transformers achieve unrivalled performance in modelling language, but remain inefficient in terms of memory and time complexity.
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech
The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones.
Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words
We investigate the performance on phoneme categorization and phoneme and word segmentation of several self-supervised learning (SSL) methods based on Contrastive Predictive Coding (CPC).
Information Retrieval for ZeroSpeech 2021: The Submission by University of Wroclaw
We present a number of low-resource approaches to the tasks of the Zero Resource Speech Challenge 2021.
Aligned Contrastive Predictive Coding
We investigate the possibility of forcing a self-supervised model trained using a contrastive predictive loss to extract slowly varying latent representations.
Representing Point Clouds with Generative Conditional Invertible Flow Networks
To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector.
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
Probabilistic Latent Variable Models (LVMs) provide an alternative to self-supervised learning approaches for linguistic representation learning from speech.
Robust Training of Vector Quantized Bottleneck Models
We show that the codebook learning can suffer from poor initialization and non-stationarity of clustered encoder outputs.
Conditional Invertible Flow for Point Cloud Generation
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Unsupervised speech representation learning using WaveNet autoencoders
We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms.
Towards Using Context-Dependent Symbols in CTC Without State-Tying Decision Trees
The embedding network is trained together with the rest of the acoustic model and removes one of the last cases in which neural systems have to be bootstrapped from GMM-HMM ones.
Efficient Purely Convolutional Text Encoding
In this work, we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences.
A Talker Ensemble: the University of Wrocław's Entry to the NIPS 2017 Conversational Intelligence Challenge
We present Poetwannabe, a chatbot submitted by the University of Wroc{\l}aw to the NIPS 2017 Conversational Intelligence Challenge, in which it ranked first ex-aequo.
On Using Backpropagation for Speech Texture Generation and Voice Conversion
Inspired by recent work on neural network image generation which rely on backpropagation towards the network inputs, we present a proof-of-concept system for speech texture synthesis and voice conversion based on two mechanisms: approximate inversion of the representation learned by a speech recognition neural network, and on matching statistics of neuron activations between different source and target utterances.
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
On Multilingual Training of Neural Dependency Parsers
We analyze the representations of characters and words that are learned by the network to establish which properties of languages were accounted for.
Sequence-to-Sequence Models Can Directly Translate Foreign Speech
We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another.
Regularizing Neural Networks by Penalizing Confident Output Distributions
We systematically explore regularizing neural networks by penalizing low entropy output distributions.
Towards better decoding and language model integration in sequence to sequence models
The recently proposed Sequence-to-Sequence (seq2seq) framework advocates replacing complex data processing pipelines, such as an entire automatic speech recognition system, with a single neural network trained in an end-to-end fashion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
There exist many problem domains where the interpretability of neural network models is essential for deployment.
Read, Tag, and Parse All at Once, or Fully-neural Dependency Parsing
We present a dependency parser implemented as a single deep neural network that reads orthographic representations of words and directly generates dependencies and their labels.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Task Loss Estimation for Sequence Prediction
Our idea is that this score can be interpreted as an estimate of the task loss, and that the estimation error may be used as a consistent surrogate loss.
End-to-End Attention-based Large Vocabulary Speech Recognition
Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs).
Attention-Based Models for Speech Recognition
Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks in- cluding machine translation, handwriting synthesis and image caption gen- eration.
Ranked #17 on
Speech Recognition
on TIMIT
Blocks and Fuel: Frameworks for deep learning
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel.
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results
We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes.
