pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Sound Source Separation Conditioned On Instrument Labels
Authors: Olga Slizovskaia, Leo Kim, Gloria Haro, Emilia Gomez
Date: 2018-11-05 00:00:00
Tasks: Music Source Separation

Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? We present an extension of the Wave-U-Net model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument labels at the bottleneck of the Wave-U-Net and show its effect on the separation results. This approach leads to other types of conditioning such as audio-visual source separation and score-informed source separation.
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
Authors: Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, Igor Mordatch
Date: 2018-11-05 00:00:00
Proceeding: ICLR 2019 5

We propose a plan online and learn offline (POLO) framework for the setting
where an agent, with an internal model, needs to continually act and learn in
the world. Our work builds on the synergistic relationship between local
model-based control, global value function learning, and exploration. We study
how local trajectory optimization can cope with approximation errors in the
value function, and can stabilize and accelerate value function learning.
Conversely, we also study how approximate value functions can help reduce the
planning horizon and allow for better policies beyond local solutions. Finally,
we also demonstrate how trajectory optimization can be used to perform
temporally coordinated exploration in conjunction with estimating uncertainty
in value function approximation. This exploration is critical for fast and
stable learning of the value function. Combining these components enable
solutions to complex simulated control tasks, like humanoid locomotion and
dexterous in-hand manipulation, in the equivalent of a few minutes of
experience in the real world.
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting
Authors: Cong Feng, Jie Zhang
Date: 2018-11-05 00:00:00
Tasks: BIG-bench Machine Learning, Load Forecasting, Model Selection, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With the growing prevalence of smart grid technology, short-term load
forecasting (STLF) becomes particularly important in power system operations.
There is a large collection of methods developed for STLF, but selecting a
suitable method under varying conditions is still challenging. This paper
develops a novel reinforcement learning based dynamic model selection (DMS)
method for STLF. A forecasting model pool is first built, including ten
state-of-the-art machine learning based forecasting models. Then a Q-learning
agent learns the optimal policy of selecting the best forecasting model for the
next time step, based on the model performance. The optimal DMS policy is
applied to select the best model at each time step with a moving window.
Numerical simulations on two-year load and weather data show that the
Q-learning algorithm converges fast, resulting in effective and efficient DMS.
The developed STLF model with Q-learning based DMS improves the forecasting
accuracy by approximately 50%, compared to the state-of-the-art machine
learning based STLF models.
Deep Genetic Network
Authors: Siddhartha Dhar Choudhury, Shashank Pandey, Kunal Mehrotra
Date: 2018-11-05 00:00:00
Tasks: Hyperparameter Optimization

Optimizing a neural network's performance is a tedious and time taking
process, this iterative process does not have any defined solution which can
work for all the problems. Optimization can be roughly categorized into -
Architecture and Hyperparameter optimization. Many algorithms have been devised
to address this problem. In this paper we introduce a neural network
architecture (Deep Genetic Network) which will optimize its parameters during
training based on its fitness. Deep Genetic Net uses genetic algorithms along
with deep neural networks to address the hyperparameter optimization problem,
this approach uses ideas like mating and mutation which are key to genetic
algorithms which help the neural net architecture to learn to optimize its
hyperparameters by itself rather than depending on a person to explicitly set
the values. Using genetic algorithms for this problem proved to work
exceptionally well when given enough time to train the network. The proposed
architecture is found to work well in optimizing hyperparameters in affine,
convolutional and recurrent layers proving to be a good choice for conventional
supervised learning tasks.
Multi-layer Relation Networks
Authors: Marius Jahrens, Thomas Martinetz
Date: 2018-11-05 00:00:00
Tasks: Relation, Relational Reasoning, Relation Network

Relational Networks (RN) as introduced by Santoro et al. (2017) have
demonstrated strong relational reasoning capabilities with a rather shallow
architecture. Its single-layer design, however, only considers pairs of
information objects, making it unsuitable for problems requiring reasoning
across a higher number of facts. To overcome this limitation, we propose a
multi-layer relation network architecture which enables successive refinements
of relational information through multiple layers. We show that the increased
depth allows for more complex relational reasoning by applying it to the bAbI
20 QA dataset, solving all 20 tasks with joint training and surpassing the
state-of-the-art results.
TzK Flow - Conditional Generative Model
Authors: Micha Livne, David J. Fleet
Date: 2018-11-05 00:00:00
Tasks: model

We introduce TzK (pronounced "task"), a conditional probability flow-based
model that exploits attributes (e.g., style, class membership, or other side
information) in order to learn tight conditional prior around manifolds of the
target observations. The model is trained via approximated ML, and offers
efficient approximation of arbitrary data sample distributions (similar to GAN
and flow-based ML), and stable training (similar to VAE and ML), while avoiding
variational approximations. TzK exploits meta-data to facilitate a bottleneck,
similar to autoencoders, thereby producing a low-dimensional representation.
Unlike autoencoders, the bottleneck does not limit model expressiveness,
similar to flow-based ML. Supervised, unsupervised, and semi-supervised
learning are supported by replacing missing observations with samples from
learned priors. We demonstrate TzK by training jointly on MNIST and Omniglot
datasets with minimal preprocessing, and weak supervision, with results
comparable to state-of-the-art.
Structured Neural Summarization
Authors: Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt
Date: 2018-11-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Source Code Summarization

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls
Authors: Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, Jason H. Li
Date: 2018-11-05 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Inference Attack

Machine learning has been applied to a broad range of applications and some
of them are available online as application programming interfaces (APIs) with
either free (trial) or paid subscriptions. In this paper, we study adversarial
machine learning in the form of back-box attacks on online classifier APIs. We
start with a deep learning based exploratory (inference) attack, which aims to
build a classifier that can provide similar classification results (labels) as
the target classifier. To minimize the difference between the labels returned
by the inferred classifier and the target classifier, we show that the deep
learning based exploratory attack requires a large number of labeled training
data samples. These labels can be collected by calling the online API, but
usually there is some strict rate limitation on the number of allowed API
calls. To mitigate the impact of limited training data, we develop an active
learning approach that first builds a classifier based on a small number of API
calls and uses this classifier to select samples to further collect their
labels. Then, a new classifier is built using more training data samples. This
updating process can be repeated multiple times. We show that this active
learning approach can build an adversarial classifier with a small statistical
difference from the target classifier using only a limited number of training
data samples. We further consider evasion and causative (poisoning) attacks
based on the inferred classifier that is built by the exploratory attack.
Evasion attack determines samples that the target classifier is likely to
misclassify, whereas causative attack provides erroneous training data samples
to reduce the reliability of the re-trained classifier. The success of these
attacks show that adversarial machine learning emerges as a feasible threat in
the realistic case with limited training data.
Confidence Propagation through CNNs for Guided Sparse Depth Regression
Authors: Abdelrahman Eldesokey, Michael Felsberg, Fahad Shahbaz Khan
Date: 2018-11-05 00:00:00
Tasks: Autonomous Driving, Depth Completion, regression

Generally, convolutional neural networks (CNNs) process data on a regular grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open research problem with numerous applications in autonomous driving, robotics, and surveillance. In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. We also propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. To integrate structural information, we also investigate fusion strategies to combine depth and RGB information in our normalized convolution network framework. In addition, we introduce the use of output confidence as an auxiliary information to improve the results. The capabilities of our normalized convolution network framework are demonstrated for the problem of scene depth completion. Comprehensive experiments are performed on the KITTI-Depth and the NYU-Depth-v2 datasets. The results clearly demonstrate that the proposed approach achieves superior performance while requiring only about 1-5% of the number of parameters compared to the state-of-the-art methods.
A human-editable Sign Language representation for software editing---and a writing system?
Authors: Michael Filhol
Date: 2018-11-05 00:00:00

To equip SL with software properly, we need an input system to represent and
manipulate signed contents in the same way that every day software allows to
process written text. Refuting the claim that video is good enough a medium to
serve the purpose, we propose to build a representation that is: editable,
queryable, synthesisable and user-friendly---we define those terms upfront. The
issue being functionally and conceptually linked to that of writing, we study
existing writing systems, namely those in use for vocal languages, those
designed and proposed for SLs, and more spontaneous ways in which SL users put
their language in writing. Observing each paradigm in turn, we move on to
propose a new approach to satisfy our goals of integration in software. We
finally open the prospect of our proposition being used outside of this
restricted scope, as a writing system in itself, and compare its properties to
the other writing systems presented.
How Reasonable are Common-Sense Reasoning Tasks: A Case-Study on the Winograd Schema Challenge and SWAG
Authors: Paul Trichelair, Ali Emami, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung
Date: 2018-11-05 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Common Sense Reasoning, Coreference Resolution

Recent studies have significantly improved the state-of-the-art on common-sense reasoning (CSR) benchmarks like the Winograd Schema Challenge (WSC) and SWAG. The question we ask in this paper is whether improved performance on these benchmarks represents genuine progress towards common-sense-enabled systems. We make case studies of both benchmarks and design protocols that clarify and qualify the results of previous work by analyzing threats to the validity of previous experimental designs. Our protocols account for several properties prevalent in common-sense benchmarks including size limitations, structural regularities, and variable instance difficulty.
Non-ergodic Convergence Analysis of Heavy-Ball Algorithms
Authors: Tao Sun, Penghang Yin, Dongsheng Li, Chun Huang, Lei Guan, Hao Jiang
Date: 2018-11-05 00:00:00

In this paper, we revisit the convergence of the Heavy-ball method, and
present improved convergence complexity results in the convex setting. We
provide the first non-ergodic O(1/k) rate result of the Heavy-ball algorithm
with constant step size for coercive objective functions. For objective
functions satisfying a relaxed strongly convex condition, the linear
convergence is established under weaker assumptions on the step size and
inertial parameter than made in the existing literature. We extend our results
to multi-block version of the algorithm with both the cyclic and stochastic
update rules. In addition, our results can also be extended to decentralized
optimization, where the ergodic analysis is not applicable.
A Biologically Plausible Learning Rule for Deep Learning in the Brain
Authors: Isabella Pozzi, Sander Bohté, Pieter Roelfsema
Date: 2018-11-05 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain. However, the methods used for deep learning by artificial neural networks are biologically unrealistic and would need to be replaced by biologically realistic counterparts. Previous biologically plausible reinforcement learning rules, like AGREL and AuGMEnT, showed promising results but focused on shallow networks with three layers. Will these learning rules also generalize to networks with more layers and can they handle tasks of higher complexity? We demonstrate the learning scheme on classical and hard image-classification benchmarks, namely MNIST, CIFAR10 and CIFAR100, cast as direct reward tasks, both for fully connected, convolutional and locally connected architectures. We show that our learning rule - Q-AGREL - performs comparably to supervised learning via error-backpropagation, with this type of trial-and-error reinforcement learning requiring only 1.5-2.5 times more epochs, even when classifying 100 different classes as in CIFAR100. Our results provide new insights into how deep learning may be implemented in the brain.
Kernel Conjugate Gradient Methods with Random Projections
Authors: Junhong Lin, Volkan Cevher
Date: 2018-11-05 00:00:00
Tasks: regression

We propose and study kernel conjugate gradient methods (KCGM) with random projections for least-squares regression over a separable Hilbert space. Considering two types of random projections generated by randomized sketches and Nystr\"{o}m subsampling, we prove optimal statistical results with respect to variants of norms for the algorithms under a suitable stopping rule. Particularly, our results show that if the projection dimension is proportional to the effective dimension of the problem, KCGM with randomized sketches can generalize optimally, while achieving a computational advantage. As a corollary, we derive optimal rates for classic KCGM in the well-conditioned regimes for the case that the target function may not be in the hypothesis space.
Decoding Generic Visual Representations From Human Brain Activity using Machine Learning
Authors: Angeliki Papadimitriou, Nikolaos Passalis, Anastasios Tefas
Date: 2018-11-05 00:00:00
Tasks: BIG-bench Machine Learning

Among the most impressive recent applications of neural decoding is the
visual representation decoding, where the category of an object that a subject
either sees or imagines is inferred by observing his/her brain activity. Even
though there is an increasing interest in the aforementioned visual
representation decoding task, there is no extensive study of the effect of
using different machine learning models on the decoding accuracy. In this paper
we provide an extensive evaluation of several machine learning models, along
with different similarity metrics, for the aforementioned task, drawing many
interesting conclusions. That way, this paper a) paves the way for developing
more advanced and accurate methods and b) provides an extensive and easily
reproducible baseline for the aforementioned decoding task.
How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks
Authors: Achim Schilling, Claus Metzner, Jonas Rietsch, Richard Gerum, Holger Schulze, Patrick Krauss
Date: 2018-11-05 00:00:00

Deep neural networks typically outperform more traditional machine learning models in their ability to classify complex data, and yet is not clear how the individual hidden layers of a deep network contribute to the overall classification performance. We thus introduce a Generalized Discrimination Value (GDV) that measures, in a non-invasive manner, how well different data classes separate in each given network layer. The GDV can be used for the automatic tuning of hyper-parameters, such as the width profile and the total depth of a network. Moreover, the layer-dependent GDV(L) provides new insights into the data transformations that self-organize during training: In the case of multi-layer perceptrons trained with error backpropagation, we find that classification of highly complex data sets requires a temporal {\em reduction} of class separability, marked by a characteristic 'energy barrier' in the initial part of the GDV(L) curve. Even more surprisingly, for a given data set, the GDV(L) is running through a fixed 'master curve', independently from the total number of network layers. Furthermore, applying the GDV to Deep Belief Networks reveals that also unsupervised training with the Contrastive Divergence method can systematically increase class separability over tens of layers, even though the system does not 'know' the desired class labels. These results indicate that the GDV may become a useful tool to open the black box of deep learning.
FUNN: Flexible Unsupervised Neural Network
Authors: David Vigouroux, Sylvain Picard
Date: 2018-11-05 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Deep neural networks have demonstrated high accuracy in image classification
tasks. However, they were shown to be weak against adversarial examples: a
small perturbation in the image which changes the classification output
dramatically. In recent years, several defenses have been proposed to solve
this issue in supervised classification tasks. We propose a method to obtain
robust features in unsupervised learning tasks against adversarial attacks. Our
method differs from existing solutions by directly learning the robust features
without the need to project the adversarial examples in the original examples
distribution space. A first auto-encoder A1 is in charge of perturbing the
input image to fool another auto-encoder A2 which is in charge of regenerating
the original image. A1 tries to find the less perturbed image under the
constraint that the error in the output of A2 should be at least equal to a
threshold. Thanks to this training, the encoder of A2 will be robust against
adversarial attacks and could be used in different tasks like classification.
Using state-of-art network architectures, we demonstrate the robustness of the
features obtained thanks to this method in classification tasks.
Learning Shared Dynamics with Meta-World Models
Authors: Lisheng Wu, Minne Li, Jun Wang
Date: 2018-11-05 00:00:00
Tasks: Atari Games, Multi-Task Learning

Humans have consciousness as the ability to perceive events and objects: a
mental model of the world developed from the most impoverished of visual
stimuli, enabling humans to make rapid decisions and take actions. Although
spatial and temporal aspects of different scenes are generally diverse, the
underlying physics among environments still work the same way, thus learning an
abstract description of shared physical dynamics helps human to understand the
world. In this paper, we explore building this mental world with neural network
models through multi-task learning, namely the meta-world model. We show
through extensive experiments that our proposed meta-world models successfully
capture the common dynamics over the compact representations of visually
different environments from Atari Games. We also demonstrate that agents
equipped with our meta-world model possess the ability of visual
self-recognition, i.e., recognize themselves from the reflected mirrored
environment derived from the classic mirror self-recognition test (MSR).
Multi-armed Bandits with Compensation
Authors: Siwei Wang, Longbo Huang
Date: 2018-11-05 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Multi-Armed Bandits

We propose and study the known-compensation multi-arm bandit (KCMAB) problem,
where a system controller offers a set of arms to many short-term players for
$T$ steps. In each step, one short-term player arrives to the system. Upon
arrival, the player aims to select an arm with the current best average reward
and receives a stochastic reward associated with the arm. In order to
incentivize players to explore other arms, the controller provides a proper
payment compensation to players. The objective of the controller is to maximize
the total reward collected by players while minimizing the compensation. We
first provide a compensation lower bound $\Theta(\sum_i {\Delta_i\log T\over
KL_i})$, where $\Delta_i$ and $KL_i$ are the expected reward gap and
Kullback-Leibler (KL) divergence between distributions of arm $i$ and the best
arm, respectively. We then analyze three algorithms to solve the KCMAB problem,
and obtain their regrets and compensations. We show that the algorithms all
achieve $O(\log T)$ regret and $O(\log T)$ compensation that match the
theoretical lower bound. Finally, we present experimental results to
demonstrate the performance of the algorithms.