ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 18 99 | paper_url stringlengths 31 31 | authors listlengths 0 8 | type stringclasses 0
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values | abstract large_stringlengths 611 1.86k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 35 | arxiv_id stringlengths 9 9 | arxiv_id_source stringclasses 1
value | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Multilingual Distributed Representations without Word Alignment | https://arxiv.org/abs/1312.6173 | [
"Karl Moritz Hermann",
"Phil Blunsom"
] | null | null | Distributed representations of meaning are a natural way to encode covariance
relationships between words and phrases in NLP. By overcoming data sparsity
problems, as well as providing information about semantic relatedness which is
not available in discrete representations, distributed representations have
proven us... | [] | null | 1 | 1312.6173 | iclr_archive | [
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Zero-Shot Learning by Convex Combination of Semantic Embeddings | https://arxiv.org/abs/1312.5650 | [
"Mohammad Norouzi",
"Tomas Mikolov",
"Samy Bengio",
"Yoram Singer",
"Jonathon Shlens",
"Andrea Frome",
"Greg S. Corrado",
"Jeffrey Dean"
] | null | null | Several recent publications have proposed methods for mapping images into
continuous semantic embedding spaces. In some cases the embedding space is
trained jointly with the image transformation. In other cases the semantic
embedding space is established by an independent natural language processing
task, and then th... | [] | null | 2 | 1312.5650 | iclr_archive | [
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks | https://arxiv.org/abs/1312.6120 | [
"Andrew M. Saxe",
"James L. McClelland",
"Surya Ganguli"
] | null | null | Despite the widespread practical success of deep learning methods, our
theoretical understanding of the dynamics of learning in deep neural networks
remains quite sparse. We attempt to bridge the gap between the theory and
practice of deep learning by systematically analyzing learning dynamics for the
restricted case... | [] | null | 3 | 1312.6120 | iclr_archive | [
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Revisiting Natural Gradient for Deep Networks | https://arxiv.org/abs/1301.3584 | [
"Razvan Pascanu",
"Yoshua Bengio"
] | null | null | We evaluate natural gradient, an algorithm originally proposed in Amari
(1997), for learning deep models. The contributions of this paper are as
follows. We show the connection between natural gradient and three other
recently proposed methods for training deep models: Hessian-Free (Martens,
2010), Krylov Subspace De... | [] | null | 4 | 1301.3584 | iclr_archive | [
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Unit Tests for Stochastic Optimization | https://arxiv.org/abs/1312.6055 | [] | null | null | Optimization by stochastic gradient descent is an important component of many
large-scale machine learning algorithms. A wide variety of such optimization
algorithms have been devised; however, it is unclear whether these algorithms
are robust and widely applicable across many different optimization landscapes.
In th... | [] | null | 5 | 1312.6055 | iclr_archive | [
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The return of AdaBoost.MH: multi-class Hamming trees | https://arxiv.org/abs/1312.6086 | [
"Balázs Kégl"
] | null | null | Within the framework of AdaBoost.MH, we propose to train vector-valued
decision trees to optimize the multi-class edge without reducing the
multi-class problem to $K$ binary one-against-all classifications. The key
element of the method is a vector-valued decision stump, factorized into an
input-independent vector of... | [] | null | 6 | 1312.6086 | iclr_archive | [
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Neuronal Synchrony in Complex-Valued Deep Networks | https://arxiv.org/abs/1312.6115 | [
"David P. Reichert",
"Thomas Serre"
] | null | null | Deep learning has recently led to great successes in tasks such as image
recognition (e.g Krizhevsky et al., 2012). However, deep networks are still
outmatched by the power and versatility of the brain, perhaps in part due to
the richer neuronal computations available to cortical circuits. The challenge
is to identif... | [] | null | 7 | 1312.6115 | iclr_archive | [
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Bounding the Test Log-Likelihood of Generative Models | https://arxiv.org/abs/1311.6184 | [
"Yoshua Bengio",
"Li Yao",
"KyungHyun Cho"
] | null | null | Several interesting generative learning algorithms involve a complex
probability distribution over many random variables, involving intractable
normalization constants or latent variable normalization. Some of them may even
not have an analytic expression for the unnormalized probability function and
no tractable app... | [] | null | 8 | 1311.6184 | iclr_archive | [
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A Generative Product-of-Filters Model of Audio | https://arxiv.org/abs/1312.5857 | [
"Dawen Liang",
"Mathew D. Hoffman",
"Gautham Mysore"
] | null | null | We propose the product-of-filters (PoF) model, a generative model that
decomposes audio spectra as sparse linear combinations of "filters" in the
log-spectral domain. PoF makes similar assumptions to those used in the classic
homomorphic filtering approach to signal processing, but replaces hand-designed
decompositio... | [] | null | 9 | 1312.5857 | iclr_archive | [
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How to Construct Deep Recurrent Neural Networks | https://arxiv.org/abs/1312.6026 | [
"Razvan Pascanu",
"Caglar Gulcehre",
"Kyunghyun Cho",
"Yoshua Bengio"
] | null | null | In this paper, we explore different ways to extend a recurrent neural network
(RNN) to a \textit{deep} RNN. We start by arguing that the concept of depth in
an RNN is not as clear as it is in feedforward neural networks. By carefully
analyzing and understanding the architecture of an RNN, however, we find three
point... | [] | null | 10 | 1312.6026 | iclr_archive | [
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Zero-Shot Learning and Clustering for Semantic Utterance Classification | https://arxiv.org/abs/1401.0509 | [
"Yann N. Dauphin",
"Gokhan Tur",
"Dilek Hakkani-Tur",
"Larry Heck"
] | null | null | We propose a novel zero-shot learning method for semantic utterance
classification (SUC). It learns a classifier $f: X \to Y$ for problems where
none of the semantic categories $Y$ are present in the training set. The
framework uncovers the link between categories and utterances using a semantic
space. We show that t... | [] | null | 11 | 1401.0509 | iclr_archive | [
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