pwc-archive/papers-with-abstracts huggingface dataset:
Deep Segment Hash Learning for Music Generation
Authors: Kevin Joslyn, Naifan Zhuang, Kien A. Hua
Date: 2018-05-30 00:00:00
Tasks: Music Generation

Music generation research has grown in popularity over the past decade,
thanks to the deep learning revolution that has redefined the landscape of
artificial intelligence. In this paper, we propose a novel approach to music
generation inspired by musical segment concatenation methods and hash learning
algorithms. Given a segment of music, we use a deep recurrent neural network
and ranking-based hash learning to assign a forward hash code to the segment to
retrieve candidate segments for continuation with matching backward hash codes.
The proposed method is thus called Deep Segment Hash Learning (DSHL). To the
best of our knowledge, DSHL is the first end-to-end segment hash learning
method for music generation, and the first to use pair-wise training with
segments of music. We demonstrate that this method is capable of generating
music which is both original and enjoyable, and that DSHL offers a promising
new direction for music generation research.
Video Summarization by Learning from Unpaired Data
Authors: Mrigank Rochan, Yang Wang
Date: 2018-05-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Video Summarization

We consider the problem of video summarization. Given an input raw video, the
goal is to select a small subset of key frames from the input video to create a
shorter summary video that best describes the content of the original video.
Most of the current state-of-the-art video summarization approaches use
supervised learning and require labeled training data. Each training instance
consists of a raw input video and its ground truth summary video curated by
human annotators. However, it is very expensive and difficult to create such
labeled training examples. To address this limitation, we propose a novel
formulation to learn video summarization from unpaired data. We present an
approach that learns to generate optimal video summaries using a set of raw
videos ($V$) and a set of summary videos ($S$), where there exists no
correspondence between $V$ and $S$. We argue that this type of data is much
easier to collect. Our model aims to learn a mapping function $F : V
\rightarrow S$ such that the distribution of resultant summary videos from
$F(V)$ is similar to the distribution of $S$ with the help of an adversarial
objective. In addition, we enforce a diversity constraint on $F(V)$ to ensure
that the generated video summaries are visually diverse. Experimental results
on two benchmark datasets indicate that our proposed approach significantly
outperforms other alternative methods.
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
Authors: Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
Date: 2018-05-30 00:00:00
Tasks: Bayesian Optimization

Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for multi-objective optimization, both Bayesian and otherwise, are aimed at recovering the Pareto front of these objectives. However, in certain cases a practitioner might desire to identify Pareto optimal points only in a subset of the Pareto front due to external considerations. In this work, we propose a strategy based on random scalarizations of the objectives that addresses this problem. Our approach is able to flexibly sample from desired regions of the Pareto front and, computationally, is considerably cheaper than most approaches for MOO. We also study a notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret. We experiment with both synthetic and real-life problems, and demonstrate superior performance of our proposed algorithm in terms of the flexibility and regret.
Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos
Authors: Naman Kohli, Daksha Yadav, Mayank Vatsa, Richa Singh, Afzel Noore
Date: 2018-05-30 00:00:00
Tasks: Kinship Verification

Identifying kinship relations has garnered interest due to several
applications such as organizing and tagging the enormous amount of videos being
uploaded on the Internet. Existing research in kinship verification primarily
focuses on kinship prediction with image pairs. In this research, we propose a
new deep learning framework for kinship verification in unconstrained videos
using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This
new autoencoder formulation introduces class-specific sparsity in the weight
matrix. The proposed three-stage SMNAE based kinship verification framework
utilizes the learned spatio-temporal representation in the video frames for
verifying kinship in a pair of videos. A new kinship video (KIVI) database of
more than 500 individuals with variations due to illumination, pose, occlusion,
ethnicity, and expression is collected for this research. It comprises a total
of 355 true kin video pairs with over 250,000 still frames. The effectiveness
of the proposed framework is demonstrated on the KIVI database and six existing
kinship databases. On the KIVI database, SMNAE yields video-based kinship
verification accuracy of 83.18% which is at least 3.2% better than existing
algorithms. The algorithm is also evaluated on six publicly available kinship
databases and compared with best-reported results. It is observed that the
proposed SMNAE consistently yields best results on all the databases
What the Vec? Towards Probabilistically Grounded Embeddings
Authors: Carl Allen, Ivana Balažević, Timothy Hospedales
Date: 2018-05-30 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Graph Embedding, Word Embeddings

Word2Vec (W2V) and GloVe are popular, fast and efficient word embedding algorithms. Their embeddings are widely used and perform well on a variety of natural language processing tasks. Moreover, W2V has recently been adopted in the field of graph embedding, where it underpins several leading algorithms. However, despite their ubiquity and relatively simple model architecture, a theoretical understanding of what the embedding parameters of W2V and GloVe learn and why that is useful in downstream tasks has been lacking. We show that different interactions between PMI vectors reflect semantic word relationships, such as similarity and paraphrasing, that are encoded in low dimensional word embeddings under a suitable projection, theoretically explaining why embeddings of W2V and GloVe work. As a consequence, we also reveal an interesting mathematical interconnection between the considered semantic relationships themselves.
On Consensus-Optimality Trade-offs in Collaborative Deep Learning
Authors: Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar
Date: 2018-05-30 00:00:00
Tasks: Deep Learning, Navigate

In distributed machine learning, where agents collaboratively learn from
diverse private data sets, there is a fundamental tension between consensus and
optimality. In this paper, we build on recent algorithmic progresses in
distributed deep learning to explore various consensus-optimality trade-offs
over a fixed communication topology. First, we propose the incremental
consensus-based distributed SGD (i-CDSGD) algorithm, which involves multiple
consensus steps (where each agent communicates information with its neighbors)
within each SGD iteration. Second, we propose the generalized consensus-based
distributed SGD (g-CDSGD) algorithm that enables us to navigate the full
spectrum from complete consensus (all agents agree) to complete disagreement
(each agent converges to individual model parameters). We analytically
establish convergence of the proposed algorithms for strongly convex and
nonconvex objective functions; we also analyze the momentum variants of the
algorithms for the strongly convex case. We support our algorithms via
numerical experiments, and demonstrate significant improvements over existing
methods for collaborative deep learning.
Neural Models for Key Phrase Detection and Question Generation
Authors: Sandeep Subramanian, Tong Wang, Xingdi Yuan, Saizheng Zhang, Yoshua Bengio, Adam Trischler
Date: 2017-06-14 00:00:00
Tasks: Question Answering, Question Generation, Question-Generation, Reading Comprehension

We propose a two-stage neural model to tackle question generation from
documents. First, our model estimates the probability that word sequences in a
document are ones that a human would pick when selecting candidate answers by
training a neural key-phrase extractor on the answers in a question-answering
corpus. Predicted key phrases then act as target answers and condition a
sequence-to-sequence question-generation model with a copy mechanism.
Empirically, our key-phrase extraction model significantly outperforms an
entity-tagging baseline and existing rule-based approaches. We further
demonstrate that our question generation system formulates fluent, answerable
questions from key phrases. This two-stage system could be used to augment or
generate reading comprehension datasets, which may be leveraged to improve
machine reading systems or in educational settings.
Amnestic Forgery: an Ontology of Conceptual Metaphors
Authors: Aldo Gangemi, Mehwish Alam, Valentina Presutti
Date: 2018-05-30 00:00:00

This paper presents Amnestic Forgery, an ontology for metaphor semantics,
based on MetaNet, which is inspired by the theory of Conceptual Metaphor.
Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology
design framework to deal with both semiotic and referential aspects of frames,
roles, mappings, and eventually blending. The description of the resource is
supplied by a discussion of its applications, with examples taken from metaphor
generation, and the referential problems of metaphoric mappings. Both schema
and data are available from the Framester SPARQL endpoint.
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Authors: Kurtland Chua, Roberto Calandra, Rowan Mcallister, Sergey Levine
Date: 2018-05-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-based reinforcement learning (RL) algorithms can attain excellent
sample efficiency, but often lag behind the best model-free algorithms in terms
of asymptotic performance. This is especially true with high-capacity
parametric function approximators, such as deep networks. In this paper, we
study how to bridge this gap, by employing uncertainty-aware dynamics models.
We propose a new algorithm called probabilistic ensembles with trajectory
sampling (PETS) that combines uncertainty-aware deep network dynamics models
with sampling-based uncertainty propagation. Our comparison to state-of-the-art
model-based and model-free deep RL algorithms shows that our approach matches
the asymptotic performance of model-free algorithms on several challenging
benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125
times fewer samples than Soft Actor Critic and Proximal Policy Optimization
respectively on the half-cheetah task).
Optimal Combination of Image Denoisers
Authors: Joon Hee Choi, Omar Elgendy, Stanley H. Chan
Date: 2017-11-17 00:00:00
Tasks: Denoising, Image Denoising

Given a set of image denoisers, each having a different denoising capability,
is there a provably optimal way of combining these denoisers to produce an
overall better result? An answer to this question is fundamental to designing
an ensemble of weak estimators for complex scenes. In this paper, we present an
optimal combination scheme by leveraging deep neural networks and convex
optimization. The proposed framework, called the Consensus Neural Network
(CsNet), introduces three new concepts in image denoising: (1) A provably
optimal procedure to combine the denoised outputs via convex optimization; (2)
A deep neural network to estimate the mean squared error (MSE) of denoised
images without needing the ground truths; (3) An image boosting procedure using
a deep neural network to improve contrast and to recover lost details of the
combined images. Experimental results show that CsNet can consistently improve
denoising performance for both deterministic and neural network denoisers.
Context-aware Cascade Attention-based RNN for Video Emotion Recognition
Authors: Man-Chin Sun, Shih-Huan Hsu, Min-Chun Yang, Jen-Hsien Chien
Date: 2018-05-30 00:00:00
Tasks: Decoder, Emotion Classification, Emotion Recognition, Machine Translation, Multimodal Emotion Recognition, Translation, Video Emotion Recognition

Emotion recognition can provide crucial information about the user in many
applications when building human-computer interaction (HCI) systems. Most of
current researches on visual emotion recognition are focusing on exploring
facial features. However, context information including surrounding environment
and human body can also provide extra clues to recognize emotion more
accurately. Inspired by "sequence to sequence model" for neural machine
translation, which models input and output sequences by an encoder and a
decoder in recurrent neural network (RNN) architecture respectively, a novel
architecture, "CACA-RNN", is proposed in this work. The proposed network
consists of two RNNs in a cascaded architecture to process both context and
facial information to perform video emotion classification. Results of the
model were submitted to video emotion recognition sub-challenge in Multimodal
Emotion Recognition Challenge (MEC2017). CACA-RNN outperforms the MEC2017
baseline (mAP of 21.7%): it achieved mAP of 45.51% on the testing set in the
video only challenge.
InfoVAE: Information Maximizing Variational Autoencoders
Authors: Shengjia Zhao, Jiaming Song, Stefano Ermon
Date: 2017-06-07 00:00:00

A key advance in learning generative models is the use of amortized inference
distributions that are jointly trained with the models. We find that existing
training objectives for variational autoencoders can lead to inaccurate
amortized inference distributions and, in some cases, improving the objective
provably degrades the inference quality. In addition, it has been observed that
variational autoencoders tend to ignore the latent variables when combined with
a decoding distribution that is too flexible. We again identify the cause in
existing training criteria and propose a new class of objectives (InfoVAE) that
mitigate these problems. We show that our model can significantly improve the
quality of the variational posterior and can make effective use of the latent
features regardless of the flexibility of the decoding distribution. Through
extensive qualitative and quantitative analyses, we demonstrate that our models
outperform competing approaches on multiple performance metrics.
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs
Authors: Sunil Thulasidasan, Jeffrey Bilmes, Garrett Kenyon
Date: 2016-12-15 00:00:00

We describe a computationally efficient, stochastic graph-regularization
technique that can be utilized for the semi-supervised training of deep neural
networks in a parallel or distributed setting. We utilize a technique, first
described in [13] for the construction of mini-batches for stochastic gradient
descent (SGD) based on synthesized partitions of an affinity graph that are
consistent with the graph structure, but also preserve enough stochasticity for
convergence of SGD to good local minima. We show how our technique allows a
graph-based semi-supervised loss function to be decomposed into a sum over
objectives, facilitating data parallelism for scalable training of machine
learning models. Empirical results indicate that our method significantly
improves classification accuracy compared to the fully-supervised case when the
fraction of labeled data is low, and in the parallel case, achieves significant
speed-up in terms of wall-clock time to convergence. We show the results for
both sequential and distributed-memory semi-supervised DNN training on a speech
corpus.
Marian: Cost-effective High-Quality Neural Machine Translation in C++
Authors: Marcin Junczys-Dowmunt, Kenneth Heafield, Hieu Hoang, Roman Grundkiewicz, Anthony Aue
Date: 2018-05-30 00:00:00
Proceeding: WS 2018 7
Tasks: CPU, GPU, Machine Translation, Translation, Vocal Bursts Intensity Prediction

This paper describes the submissions of the "Marian" team to the WNMT 2018
shared task. We investigate combinations of teacher-student training,
low-precision matrix products, auto-tuning and other methods to optimize the
Transformer model on GPU and CPU. By further integrating these methods with the
new averaging attention networks, a recently introduced faster Transformer
variant, we create a number of high-quality, high-performance models on the GPU
and CPU, dominating the Pareto frontier for this shared task.
Generalised Structural CNNs (SCNNs) for time series data with arbitrary graph topology
Authors: Thomas Teh, Chaiyawan Auepanwiriyakul, John Alexander Harston, A. Aldo Faisal
Date: 2018-03-14 00:00:00
Tasks: Time Series, Time Series Analysis

Deep Learning methods, specifically convolutional neural networks (CNNs),
have seen a lot of success in the domain of image-based data, where the data
offers a clearly structured topology in the regular lattice of pixels. This
4-neighbourhood topological simplicity makes the application of convolutional
masks straightforward for time series data, such as video applications, but
many high-dimensional time series data are not organised in regular lattices,
and instead values may have adjacency relationships with non-trivial
topologies, such as small-world networks or trees. In our application case,
human kinematics, it is currently unclear how to generalise convolutional
kernels in a principled manner. Therefore we define and implement here a
framework for general graph-structured CNNs for time series analysis. Our
algorithm automatically builds convolutional layers using the specified
adjacency matrix of the data dimensions and convolutional masks that scale with
the hop distance. In the limit of a lattice-topology our method produces the
well-known image convolutional masks. We test our method first on synthetic
data of arbitrarily-connected graphs and human hand motion capture data, where
the hand is represented by a tree capturing the mechanical dependencies of the
joints. We are able to demonstrate, amongst other things, that inclusion of the
graph structure of the data dimensions improves model prediction significantly,
when compared against a benchmark CNN model with only time convolution layers.
Problem-Adapted Artificial Intelligence for Online Network Optimization
Authors: Spyridon Vassilaras, Luigi Vigneri, Nikolaos Liakopoulos, Georgios S. Paschos, Apostolos Destounis, Thrasyvoulos Spyropoulos, Merouane Debbah
Date: 2018-05-30 00:00:00
Tasks: Management

Future 5G wireless networks will rely on agile and automated network
management, where the usage of diverse resources must be jointly optimized with
surgical accuracy. A number of key wireless network functionalities (e.g.,
traffic steering, power control) give rise to hard optimization problems. What
is more, high spatio-temporal traffic variability coupled with the need to
satisfy strict per slice/service SLAs in modern networks, suggest that these
problems must be constantly (re-)solved, to maintain close-to-optimal
performance. To this end, we propose the framework of Online Network
Optimization (ONO), which seeks to maintain both agile and efficient control
over time, using an arsenal of data-driven, online learning, and AI-based
techniques. Since the mathematical tools and the studied regimes vary widely
among these methodologies, a theoretical comparison is often out of reach.
Therefore, the important question `what is the right ONO technique?' remains
open to date. In this paper, we discuss the pros and cons of each technique and
present a direct quantitative comparison for a specific use case, using real
data. Our results suggest that carefully combining the insights of problem
modeling with state-of-the-art AI techniques provides significant advantages at
reasonable complexity.
MPDCompress - Matrix Permutation Decomposition Algorithm for Deep Neural Network Compression
Authors: Lazar Supic, Rawan Naous, Ranko Sredojevic, Aleksandra Faust, Vladimir Stojanovic
Date: 2018-05-30 00:00:00
Tasks: Neural Network Compression, Quantization

Deep neural networks (DNNs) have become the state-of-the-art technique for
machine learning tasks in various applications. However, due to their size and
the computational complexity, large DNNs are not readily deployable on edge
devices in real-time. To manage complexity and accelerate computation, network
compression techniques based on pruning and quantization have been proposed and
shown to be effective in reducing network size. However, such network
compression can result in irregular matrix structures that are mismatched with
modern hardware-accelerated platforms, such as graphics processing units (GPUs)
designed to perform the DNN matrix multiplications in a structured
(block-based) way. We propose MPDCompress, a DNN compression algorithm based on
matrix permutation decomposition via random mask generation. In-training
application of the masks molds the synaptic weight connection matrix to a
sub-graph separation format. Aided by the random permutations, a
hardware-desirable block matrix is generated, allowing for a more efficient
implementation and compression of the network. To show versatility, we
empirically verify MPDCompress on several network models, compression rates,
and image datasets. On the LeNet 300-100 model (MNIST dataset), Deep MNIST, and
CIFAR10, we achieve 10 X network compression with less than 1% accuracy loss
compared to non-compressed accuracy performance. On AlexNet for the full
ImageNet ILSVRC-2012 dataset, we achieve 8 X network compression with less than
1% accuracy loss, with top-5 and top-1 accuracies of 79.6% and 56.4%,
respectively. Finally, we observe that the algorithm can offer inference
speedups across various hardware platforms, with 4 X faster operation achieved
on several mobile GPUs.
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks
Authors: Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann Lecun, Nathan Srebro
Date: 2018-05-30 00:00:00

Despite existing work on ensuring generalization of neural networks in terms
of scale sensitive complexity measures, such as norms, margin and sharpness,
these complexity measures do not offer an explanation of why neural networks
generalize better with over-parametrization. In this work we suggest a novel
complexity measure based on unit-wise capacities resulting in a tighter
generalization bound for two layer ReLU networks. Our capacity bound correlates
with the behavior of test error with increasing network sizes, and could
potentially explain the improvement in generalization with
over-parametrization. We further present a matching lower bound for the
Rademacher complexity that improves over previous capacity lower bounds for
neural networks.