Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and re… (see more)presentations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capacity and could also facilitate noise-robust memory retrieval mechanisms. Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models. In this work, we provide the key atomic components for complex-valued deep neural networks and apply them to convolutional feed-forward networks and convolutional LSTMs. More precisely, we rely on complex convolutions and present algorithms for complex batch-normalization, complex weight initialization strategies for complex-valued neural nets and we use them in experiments with end-to-end training schemes. We demonstrate that such complex-valued models are competitive with their real-valued counterparts. We test deep complex models on several computer vision tasks, on music transcription using the MusicNet dataset and on Speech Spectrum Prediction using the TIMIT dataset. We achieve state-of-the-art performance on these audio-related tasks.
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Deep Complex Networks
Chiheb Trabelsi
Olexa Bilaniuk
Dmitriy Serdyuk
Sandeep Subramanian
Joao Felipe Santos
Soroush Mehri
Implementation of Sparse Superposition Codes
Carlo Condo
Sparse superposition codes (SSCs) are capacity achieving codes whose decoding process is a linear sensing problem. Decoding approaches thus … (see more)exploit the approximate message passing algorithm, which has been proven to be effective in compressing sensing. Previous work from the authors has evaluated the error correction performance of SSCs under finite precision and finite code length. This paper proposes the first SSC encoder and decoder architectures in the literature. The architectures are parametrized and applicable to all SSCs: A set of wide-ranging case studies is then considered, and code-specific approximations, along with implementation results in 65 nm CMOS technology, are then provided. The encoding process can be carried out with low power consumption (
Implementation of Sparse Superposition Codes
Carlo Condo
Sparse superposition codes (SSCs) are capacity achieving codes whose decoding process is a linear sensing problem. Decoding approaches thus … (see more)exploit the approximate message passing algorithm, which has been proven to be effective in compressing sensing. Previous work from the authors has evaluated the error correction performance of SSCs under finite precision and finite code length. This paper proposes the first SSC encoder and decoder architectures in the literature. The architectures are parametrized and applicable to all SSCs: A set of wide-ranging case studies is then considered, and code-specific approximations, along with implementation results in 65 nm CMOS technology, are then provided. The encoding process can be carried out with low power consumption (≤2.103 mW), while the semi-parallel decoder architecture can reach a throughput of 1.3 Gb/s with a 768 × 6-bit SSC codeword and an area occupation of 2.43 mm2.
Multi-modal Variational Encoder-Decoders
Iulian V. Serban
Alexander G. Ororbia II
Recent advances in neural variational inference have facilitated efficient training of powerful directed graphical models with continuous la… (see more)tent variables, such as variational autoencoders. However, these models usually assume simple, uni-modal priors — such as the multivariate Gaussian distribution — yet many real-world data distributions are highly complex and multi-modal. Examples of complex and multi-modal distributions range from topics in newswire text to conversational dialogue responses. When such latent variable models are applied to these domains, the restriction of the simple, uni-modal prior hinders the overall expressivity of the learned model as it cannot possibly capture more complex aspects of the data distribution. To overcome this critical restriction, we propose a flexible, simple prior distribution which can be learned efficiently and potentially capture an exponential number of modes of a target distribution. We develop the multi-modal variational encoder-decoder framework and investigate the effectiveness of the proposed prior in several natural language processing modeling tasks, including document modeling and dialogue modeling.