ICLR
Collection
Accepted papers for ICLR (International Conference on Learning Representations), one dataset per year. • 14 items • Updated
title stringlengths 15 81 | paper_url stringlengths 31 31 | authors listlengths 1 6 | type stringclasses 2
values | primary_area stringclasses 0
values | abstract large_stringlengths 616 1.37k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 42 | arxiv_id stringlengths 9 9 | arxiv_id_source stringclasses 1
value | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Word Representations via Gaussian Embedding | https://arxiv.org/abs/1412.6623 | [
"Luke Vilnis",
"Andrew McCallum"
] | Oral | null | Current work in lexical distributed representations maps each word to a point
vector in low-dimensional space. Mapping instead to a density provides many
interesting advantages, including better capturing uncertainty about a
representation and its relationships, expressing asymmetries more naturally
than dot product ... | [] | null | 1 | 1412.6623 | iclr_archive | [
-0.022525973618030548,
-0.007958680391311646,
0.0029189216438680887,
0.06130314990878105,
0.026897192001342773,
0.05592765286564827,
0.02717588283121586,
0.0026656747795641422,
-0.002198993694037199,
-0.03068809024989605,
-0.009585809893906116,
0.015337381511926651,
-0.07212560623884201,
0... |
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN) | https://arxiv.org/abs/1412.6632 | [
"Junhua Mao",
"Wei Xu",
"Yi Yang",
"Jiang Wang",
"Alan Yuille"
] | Oral | null | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel image captions. It directly models the probability
distribution of generating a word given previous words and an image. Image
captions are generated by sampling from this distribution. The model consists
of two sub-netw... | [] | null | 2 | 1412.6632 | iclr_archive | [
-0.009589101187884808,
-0.041977301239967346,
-0.015281761065125465,
0.07077564299106598,
0.03211687132716179,
0.03752228990197182,
0.00904516689479351,
0.0399647057056427,
-0.05049708113074303,
-0.01798888109624386,
-0.03508518263697624,
0.029002578929066658,
-0.05790695920586586,
-0.0007... |
Deep Structured Output Learning for Unconstrained Text Recognition | https://arxiv.org/abs/1412.5903 | [
"Max Jaderberg",
"Karen Simonyan",
"Andrea Vedaldi",
"Andrew Zisserman"
] | Oral | null | We develop a representation suitable for the unconstrained recognition of
words in natural images: the general case of no fixed lexicon and unknown
length.
To this end we propose a convolutional neural network (CNN) based
architecture which incorporates a Conditional Random Field (CRF) graphical
model, taking the w... | [] | null | 3 | 1412.5903 | iclr_archive | [
-0.00413596211001277,
-0.027735279873013496,
-0.004071063827723265,
0.0515449196100235,
0.033686406910419464,
0.029282517731189728,
-0.0011436325730755925,
0.03766223043203354,
0.0021310923621058464,
-0.02687574177980423,
-0.027515815570950508,
0.033219192177057266,
-0.0742066279053688,
-0... |
Very Deep Convolutional Networks for Large-Scale Image Recognition | https://arxiv.org/abs/1409.1556 | [
"Karen Simonyan",
"Andrew Zisserman"
] | Oral | null | In this work we investigate the effect of the convolutional network depth on
its accuracy in the large-scale image recognition setting. Our main
contribution is a thorough evaluation of networks of increasing depth using an
architecture with very small (3x3) convolution filters, which shows that a
significant improve... | [] | null | 4 | 1409.1556 | iclr_archive | [
0.01817971095442772,
-0.0473996140062809,
0.004819185007363558,
0.02936873771250248,
0.03162720799446106,
0.015696119517087936,
0.008704915642738342,
0.022175777703523636,
-0.01362753938883543,
-0.054987773299217224,
0.0107796099036932,
-0.020215651020407677,
-0.07133769243955612,
0.024413... |
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation | https://arxiv.org/abs/1412.7580 | [
"Nicolas Vasilache",
"Jeff Johnson",
"Michael Mathieu",
"Soumith Chintala",
"Serkan Piantino",
"Yann LeCun"
] | Oral | null | We examine the performance profile of Convolutional Neural Network training
on the current generation of NVIDIA Graphics Processing Units. We introduce two
new Fast Fourier Transform convolution implementations: one based on NVIDIA's
cuFFT library, and another based on a Facebook authored FFT implementation,
fbfft, t... | [] | null | 5 | 1412.7580 | iclr_archive | [
0.005991718731820583,
-0.04726449027657509,
0.025601275265216827,
0.025315532460808754,
0.03517220914363861,
0.03579822927713394,
-0.012910126708447933,
0.049497686326503754,
-0.007204278372228146,
-0.05632399022579193,
0.015573403798043728,
0.0014487336156889796,
-0.07538507133722305,
-0.... |
Reweighted Wake-Sleep | https://arxiv.org/abs/1406.2751 | [
"Jorg Bornschein",
"Yoshua Bengio"
] | Oral | null | Training deep directed graphical models with many hidden variables and
performing inference remains a major challenge. Helmholtz machines and deep
belief networks are such models, and the wake-sleep algorithm has been proposed
to train them. The wake-sleep algorithm relies on training not just the
directed generative... | [] | null | 6 | 1406.2751 | iclr_archive | [
-0.006134308874607086,
-0.0039626797661185265,
-0.010186558589339256,
0.031610701233148575,
0.028113123029470444,
0.01993045024573803,
0.04788599908351898,
0.012760824523866177,
0.00452772993594408,
-0.04028919339179993,
-0.008980482816696167,
0.017194831743836403,
-0.06251934915781021,
-0... |
The local low-dimensionality of natural images | https://arxiv.org/abs/1412.6626 | [
"Olivier Henaff",
"Johannes Balle",
"Neil Rabinowitz",
"Eero Simoncelli"
] | Oral | null | We develop a new statistical model for photographic images, in which the
local responses of a bank of linear filters are described as jointly Gaussian,
with zero mean and a covariance that varies slowly over spatial position. We
optimize sets of filters so as to minimize the nuclear norms of matrices of
their local a... | [] | null | 7 | 1412.6626 | iclr_archive | [
-0.0010592426406219602,
-0.0028493625577539206,
0.016646448522806168,
0.0419737882912159,
0.0419563390314579,
0.04619161784648895,
0.008415493182837963,
-0.019615905359387398,
-0.06176786497235298,
-0.07221227884292603,
-0.006884079892188311,
-0.010244947858154774,
-0.0753149539232254,
-0.... |
Memory Networks | https://arxiv.org/abs/1410.3916 | [
"Jason Weston",
"Sumit Chopra",
"Antoine Bordes"
] | Oral | null | We describe a new class of learning models called memory networks. Memory
networks reason with inference components combined with a long-term memory
component; they learn how to use these jointly. The long-term memory can be
read and written to, with the goal of using it for prediction. We investigate
these models in... | [] | null | 8 | 1410.3916 | iclr_archive | [
-0.009670330211520195,
0.001341706607490778,
-0.009222445078194141,
0.0511188805103302,
0.04864468052983284,
0.01231493428349495,
-0.005306434817612171,
0.021064434200525284,
-0.0411897748708725,
0.010568782687187195,
0.0015729879960417747,
0.024641918018460274,
-0.04645440727472305,
-0.01... |
Object detectors emerge in Deep Scene CNNs | https://arxiv.org/abs/1412.6856 | [
"Bolei Zhou",
"Aditya Khosla",
"Agata Lapedriza",
"Aude Oliva",
"Antonio Torralba"
] | Oral | null | With the success of new computational architectures for visual processing,
such as convolutional neural networks (CNN) and access to image databases with
millions of labeled examples (e.g., ImageNet, Places), the state of the art in
computer vision is advancing rapidly. One important factor for continued
progress is ... | [] | null | 9 | 1412.6856 | iclr_archive | [
-0.006357009056955576,
0.001259227399714291,
0.02100524492561817,
0.04599931463599205,
0.024247700348496437,
0.010110636241734028,
0.010036276653409004,
0.01003933697938919,
-0.04300607740879059,
-0.03792363777756691,
-0.03891061991453171,
0.006014237646013498,
-0.05411538854241371,
-0.002... |
Qualitatively characterizing neural network optimization problems | https://arxiv.org/abs/1412.6544 | [
"Ian Goodfellow",
"Oriol Vinyals"
] | Oral | null | Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
a... | [] | null | 10 | 1412.6544 | iclr_archive | [
-0.04026986286044121,
-0.022642264142632484,
-0.005983470473438501,
0.04811333492398262,
0.03697055205702782,
0.050341468304395676,
-0.0063139148987829685,
-0.016202392056584358,
-0.03586403653025627,
-0.03219089284539223,
-0.019967157393693924,
0.022246871143579483,
-0.0397377647459507,
0... |
Neural Machine Translation by Jointly Learning to Align and Translate | https://arxiv.org/abs/1409.0473 | [
"Dzmitry Bahdanau",
"Kyunghyun Cho",
"Yoshua Bengio"
] | Oral | null | Neural machine translation is a recently proposed approach to machine
translation. Unlike the traditional statistical machine translation, the neural
machine translation aims at building a single neural network that can be
jointly tuned to maximize the translation performance. The models proposed
recently for neural ... | [] | null | 11 | 1409.0473 | iclr_archive | [
-0.005684528965502977,
-0.03467094525694847,
-0.007944873534142971,
0.018774930387735367,
0.016290921717882156,
0.06396574527025223,
0.015409497544169426,
0.02249527908861637,
-0.01750517264008522,
-0.04119409993290901,
-0.031601957976818085,
0.03298115357756615,
-0.03783518448472023,
0.00... |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 32