pwc-archive/papers-with-abstracts huggingface dataset:
Learning with Random Learning Rates
Authors: Léonard Blier, Pierre Wolinski, Yann Ollivier
Date: 2018-10-02 00:00:00

Hyperparameter tuning is a bothersome step in the training of deep learning
models. One of the most sensitive hyperparameters is the learning rate of the
gradient descent. We present the 'All Learning Rates At Once' (Alrao)
optimization method for neural networks: each unit or feature in the network
gets its own learning rate sampled from a random distribution spanning several
orders of magnitude. This comes at practically no computational cost. Perhaps
surprisingly, stochastic gradient descent (SGD) with Alrao performs close to
SGD with an optimally tuned learning rate, for various architectures and
problems. Alrao could save time when testing deep learning models: a range of
models could be quickly assessed with Alrao, and the most promising models
could then be trained more extensively. This text comes with a PyTorch
implementation of the method, which can be plugged on an existing PyTorch
model: https://github.com/leonardblier/alrao .
Landmine Detection Using Autoencoders on Multi-polarization GPR Volumetric Data
Authors: Paolo Bestagini, Federico Lombardi, Maurizio Lualdi, Francesco Picetti, Stefano Tubaro
Date: 2018-10-02 00:00:00
Tasks: Anomaly Detection, GPR, Landmine, object-detection, Object Detection

Buried landmines and unexploded remnants of war are a constant threat for the
population of many countries that have been hit by wars in the past years. The
huge amount of human lives lost due to this phenomenon has been a strong
motivation for the research community toward the development of safe and robust
techniques designed for landmine clearance. Nonetheless, being able to detect
and localize buried landmines with high precision in an automatic fashion is
still considered a challenging task due to the many different boundary
conditions that characterize this problem (e.g., several kinds of objects to
detect, different soils and meteorological conditions, etc.). In this paper, we
propose a novel technique for buried object detection tailored to unexploded
landmine discovery. The proposed solution exploits a specific kind of
convolutional neural network (CNN) known as autoencoder to analyze volumetric
data acquired with ground penetrating radar (GPR) using different
polarizations. This method works in an anomaly detection framework, indeed we
only train the autoencoder on GPR data acquired on landmine-free areas. The
system then recognizes landmines as objects that are dissimilar to the soil
used during the training step. Experiments conducted on real data show that the
proposed technique requires little training and no ad-hoc data pre-processing
to achieve accuracy higher than 93% on challenging datasets.
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Authors: Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: 2D Human Pose Estimation, continuous-control, Continuous Control, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

We study the problem of representation learning in goal-conditioned
hierarchical reinforcement learning. In such hierarchical structures, a
higher-level controller solves tasks by iteratively communicating goals which a
lower-level policy is trained to reach. Accordingly, the choice of
representation -- the mapping of observation space to goal space -- is crucial.
To study this problem, we develop a notion of sub-optimality of a
representation, defined in terms of expected reward of the optimal hierarchical
policy using this representation. We derive expressions which bound the
sub-optimality and show how these expressions can be translated to
representation learning objectives which may be optimized in practice. Results
on a number of difficult continuous-control tasks show that our approach to
representation learning yields qualitatively better representations as well as
quantitatively better hierarchical policies, compared to existing methods (see
videos at https://sites.google.com/view/representation-hrl).
A Deep Autoencoder System for Differentiation of Cancer Types Based on DNA Methylation State
Authors: Mohammed Khwaja, Melpomeni Kalofonou, Chris Toumazou
Date: 2018-10-02 00:00:00
Tasks: Retrieval, Specificity

A Deep Autoencoder based content retrieval algorithm is proposed for
prediction and differentiation of cancer types based on the presence of
epigenetic patterns of DNA methylation identified in genetic regions known as
CpG islands. The developed deep learning system uses a CpG island state
classification sub-system to complete sets of missing/incomplete island data in
given human cell lines, and is then pipelined with an intricate set of
statistical and signal processing methods to accurately predict the presence of
cancer and further differentiate the type and cell of origin in the event of a
positive result. The proposed system was trained with previously reported data
derived from four case groups of cancer cell lines, achieving overall
Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75% and Matthews
Correlation Coefficient of 0.687. The ability to predict and differentiate
cancer types using epigenetic events as the identifying patterns was
demonstrated in previously reported data sets from breast, lung, lymphoblastic
leukemia and urological cancer cell lines, allowing the pipelined system to be
robust and adjustable to other cancer cell lines or epigenetic events.
CEM-RL: Combining evolutionary and gradient-based methods for policy search
Authors: Aloïs Pourchot, Olivier Sigaud
Date: 2018-10-02 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are
two popular approaches to policy search. The former is widely applicable and
rather stable, but suffers from low sample efficiency. By contrast, the latter
is more sample efficient, but the most sample efficient variants are also
rather unstable and highly sensitive to hyper-parameter setting. So far, these
families of methods have mostly been compared as competing tools. However, an
emerging approach consists in combining them so as to get the best of both
worlds. Two previously existing combinations use either an ad hoc evolutionary
algorithm or a goal exploration process together with the Deep Deterministic
Policy Gradient (DDPG) algorithm, a sample efficient off-policy deep RL
algorithm. In this paper, we propose a different combination scheme using the
simple cross-entropy method (CEM) and Twin Delayed Deep Deterministic policy
gradient (td3), another off-policy deep RL algorithm which improves over ddpg.
We evaluate the resulting method, cem-rl, on a set of benchmarks classically
used in deep RL. We show that cem-rl benefits from several advantages over its
competitors and offers a satisfactory trade-off between performance and sample
efficiency.
Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation
Authors: John Martin, Jinkun Wang, Brendan Englot
Date: 2018-10-02 00:00:00
Tasks: Marine Robot Navigation, Navigate, Robot Navigation

We present a method for Temporal Difference (TD) learning that addresses
several challenges faced by robots learning to navigate in a marine
environment. For improved data efficiency, our method reduces TD updates to
Gaussian Process regression. To make predictions amenable to online settings,
we introduce a sparse approximation with improved quality over current
rejection-based sparse methods. We derive the predictive value function
posterior and use the moments to obtain a new algorithm for model-free policy
evaluation, SPGP-SARSA. With simple changes, we show SPGP-SARSA can be reduced
to a model-based equivalent, SPGP-TD. We perform comprehensive simulation
studies and also conduct physical learning trials with an underwater robot. Our
results show SPGP-SARSA can outperform the state-of-the-art sparse method,
replicate the prediction quality of its exact counterpart, and be applied to
solve underwater navigation tasks.
Nth Absolute Root Mean Error
Authors: Siddhartha Dhar Choudhury, Shashank Pandey
Date: 2018-09-30 00:00:00
Tasks: regression

Neural network training process takes long time when the size of training
data is huge, without the large set of training values the neural network is
unable to learn features. This dilemma between time and size of data is often
solved using fast GPUs, but we present a better solution for a subset of those
problems. To reduce the time for training a regression model using neural
network we introduce a loss function called Nth Absolute Root Mean Error
(NARME). It helps to train regression models much faster compared to other
existing loss functions. Experiments show that in most use cases NARME reduces
the required number of epochs to almost one-tenth of that required by other
commonly used loss functions, and also achieves great accuracy in the small
amount of time in which it was trained.
Characterization of Visual Object Representations in Rat Primary Visual Cortex
Authors: Sebastiano Vascon, Ylenia Parin, Eis Annavini, Mattia D'Andola, Davide Zoccolan, Marcello Pelillo
Date: 2018-10-02 00:00:00
Tasks: Clustering, General Classification, Position

For most animal species, quick and reliable identification of visual objects
is critical for survival. This applies also to rodents, which, in recent years,
have become increasingly popular models of visual functions. For this reason in
this work we analyzed how various properties of visual objects are represented
in rat primary visual cortex (V1). The analysis has been carried out through
supervised (classification) and unsupervised (clustering) learning methods. We
assessed quantitatively the discrimination capabilities of V1 neurons by
demonstrating how photometric properties (luminosity and object position in the
scene) can be derived directly from the neuronal responses.
Inference Over Programs That Make Predictions
Authors: Yura Perov
Date: 2018-10-02 00:00:00
Tasks: Probabilistic Programming, Program induction, Program Synthesis

This abstract extends on the previous work (arXiv:1407.2646,
arXiv:1606.00075) on program induction using probabilistic programming. It
describes possible further steps to extend that work, such that, ultimately,
automatic probabilistic program synthesis can generalise over any reasonable
set of inputs and outputs, in particular in regard to text, image and video
data.
Thompson Sampling Algorithms for Cascading Bandits
Authors: Zixin Zhong, Wang Chi Cheung, Vincent Y. F. Tan
Date: 2018-10-02 00:00:00
Tasks: Efficient Exploration, Multi-Armed Bandits, Recommendation Systems, Thompson Sampling

Motivated by the pressing need for efficient optimization in online recommender systems, we revisit the cascading bandit model proposed by Kveton et al. (2015). While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only known for the latter. In this paper, we first provide a problem-dependent upper bound on the regret of a TS algorithm with Beta-Bernoulli updates; this upper bound is tighter than a recent derivation under a more general setting by Huyuk and Tekin (2019). Next, we design and analyze another TS algorithm with Gaussian updates, TS-Cascade. TS-Cascade achieves the state-of-the-art regret bound for cascading bandits. Complementarily, we consider a linear generalization of the cascading bandit model, which allows efficient learning in large cascading bandit problem instances. We introduce and analyze a TS algorithm, which enjoys a regret bound that depends on the dimension of the linear model but not the number of items. Finally, by using information-theoretic techniques and judiciously constructing cascading bandit instances, we derive a nearly matching regret lower bound for the standard model. Our paper establishes the first theoretical guarantees on TS algorithms for stochastic combinatorial bandit problem model with partial feedback. Numerical experiments demonstrate the superiority of the proposed TS algorithms compared to existing UCB-based ones.
Adversarial Examples - A Complete Characterisation of the Phenomenon
Authors: Alexandru Constantin Serban, Erik Poll, Joost Visser
Date: 2018-10-02 00:00:00
Tasks: BIG-bench Machine Learning

We provide a complete characterisation of the phenomenon of adversarial
examples - inputs intentionally crafted to fool machine learning models. We aim
to cover all the important concerns in this field of study: (1) the conjectures
on the existence of adversarial examples, (2) the security, safety and
robustness implications, (3) the methods used to generate and (4) protect
against adversarial examples and (5) the ability of adversarial examples to
transfer between different machine learning models. We provide ample background
information in an effort to make this document self-contained. Therefore, this
document can be used as survey, tutorial or as a catalog of attacks and
defences using adversarial examples.
EMI: Exploration with Mutual Information
Authors: Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song
Date: 2018-10-02 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or discriminative modeling of novelty. We propose EMI, which is an exploration method that constructs embedding representation of states and actions that does not rely on generative decoding of the full observation but extracts predictive signals that can be used to guide exploration based on forward prediction in the representation space. Our experiments show competitive results on challenging locomotion tasks with continuous control and on image-based exploration tasks with discrete actions on Atari. The source code is available at https://github.com/snu-mllab/EMI .
Findings of the E2E NLG Challenge
Authors: Ondřej Dušek, Jekaterina Novikova, Verena Rieser
Date: 2018-10-02 00:00:00
Proceeding: WS 2018 11
Tasks: Data-to-Text Generation, Spoken Dialogue Systems, Text Generation

This paper summarises the experimental setup and results of the first shared
task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue
systems. Recent end-to-end generation systems are promising since they reduce
the need for data annotation. However, they are currently limited to small,
delexicalised datasets. The E2E NLG shared task aims to assess whether these
novel approaches can generate better-quality output by learning from a dataset
containing higher lexical richness, syntactic complexity and diverse discourse
phenomena. We compare 62 systems submitted by 17 institutions, covering a wide
range of approaches, including machine learning architectures -- with the
majority implementing sequence-to-sequence models (seq2seq) -- as well as
systems based on grammatical rules and templates.
Semi-supervised Text Regression with Conditional Generative Adversarial Networks
Authors: Tao Li, Xudong Liu, Shih-An Su
Date: 2018-10-02 00:00:00
Tasks: Generative Adversarial Network, regression, Semi-Supervised Text Regression

Enormous online textual information provides intriguing opportunities for
understandings of social and economic semantics. In this paper, we propose a
novel text regression model based on a conditional generative adversarial
network (GAN), with an attempt to associate textual data and social outcomes in
a semi-supervised manner. Besides promising potential of predicting
capabilities, our superiorities are twofold: (i) the model works with
unbalanced datasets of limited labelled data, which align with real-world
scenarios; and (ii) predictions are obtained by an end-to-end framework,
without explicitly selecting high-level representations. Finally we point out
related datasets for experiments and future research directions.
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
Authors: Bharath Bhushan Damodaran, Rémi Flamary, Viven Seguy, Nicolas Courty
Date: 2018-10-02 00:00:00
Tasks: Classification, General Classification

Deep neural networks have established as a powerful tool for large scale
supervised classification tasks. The state-of-the-art performances of deep
neural networks are conditioned to the availability of large number of
accurately labeled samples. In practice, collecting large scale accurately
labeled datasets is a challenging and tedious task in most scenarios of remote
sensing image analysis, thus cheap surrogate procedures are employed to label
the dataset. Training deep neural networks on such datasets with inaccurate
labels easily overfits to the noisy training labels and degrades the
performance of the classification tasks drastically. To mitigate this effect,
we propose an original solution with entropic optimal transportation. It allows
to learn in an end-to-end fashion deep neural networks that are, to some
extent, robust to inaccurately labeled samples. We empirically demonstrate on
several remote sensing datasets, where both scene and pixel-based hyperspectral
images are considered for classification. Our method proves to be highly
tolerant to significant amounts of label noise and achieves favorable results
against state-of-the-art methods.
Learning Discriminators as Energy Networks in Adversarial Learning
Authors: Pingbo Pan, Yan Yan, Tianbao Yang, Yi Yang
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Segmentation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Prediction, Semantic Segmentation, Structured Prediction

We propose a novel framework for structured prediction via adversarial
learning. Existing adversarial learning methods involve two separate networks,
i.e., the structured prediction models and the discriminative models, in the
training. The information captured by discriminative models complements that in
the structured prediction models, but few existing researches have studied on
utilizing such information to improve structured prediction models at the
inference stage. In this work, we propose to refine the predictions of
structured prediction models by effectively integrating discriminative models
into the prediction. Discriminative models are treated as energy-based models.
Similar to the adversarial learning, discriminative models are trained to
estimate scores which measure the quality of predicted outputs, while
structured prediction models are trained to predict contrastive outputs with
maximal energy scores. In this way, the gradient vanishing problem is
ameliorated, and thus we are able to perform inference by following the ascent
gradient directions of discriminative models to refine structured prediction
models. The proposed method is able to handle a range of tasks, e.g.,
multi-label classification and image segmentation. Empirical results on these
two tasks validate the effectiveness of our learning method.
Know What Your Neighbors Do: 3D Semantic Segmentation of Point Clouds
Authors: Francis Engelmann, Theodora Kontogianni, Jonas Schult, Bastian Leibe
Date: 2018-10-02 00:00:00
Tasks: 3D Semantic Segmentation, Segmentation, Semantic Segmentation

In this paper, we present a deep learning architecture which addresses the
problem of 3D semantic segmentation of unstructured point clouds. Compared to
previous work, we introduce grouping techniques which define point
neighborhoods in the initial world space and the learned feature space.
Neighborhoods are important as they allow to compute local or global point
features depending on the spatial extend of the neighborhood. Additionally, we
incorporate dedicated loss functions to further structure the learned point
feature space: the pairwise distance loss and the centroid loss. We show how to
apply these mechanisms to the task of 3D semantic segmentation of point clouds
and report state-of-the-art performance on indoor and outdoor datasets.
Training compact deep learning models for video classification using circulant matrices
Authors: Alexandre Araujo, Benjamin Negrevergne, Yann Chevaleyre, Jamal Atif
Date: 2018-10-02 00:00:00
Tasks: General Classification, Video Classification

In real world scenarios, model accuracy is hardly the only factor to
consider. Large models consume more memory and are computationally more
intensive, which makes them difficult to train and to deploy, especially on
mobile devices. In this paper, we build on recent results at the crossroads of
Linear Algebra and Deep Learning which demonstrate how imposing a structure on
large weight matrices can be used to reduce the size of the model. We propose
very compact models for video classification based on state-of-the-art network
architectures such as Deep Bag-of-Frames, NetVLAD and NetFisherVectors. We then
conduct thorough experiments using the large YouTube-8M video classification
dataset. As we will show, the circulant DBoF embedding achieves an excellent
trade-off between size and accuracy.
Time Reversal as Self-Supervision
Authors: Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar
Date: 2018-10-02 00:00:00
Tasks: Model Predictive Control

A longstanding challenge in robot learning for manipulation tasks has been the ability to generalize to varying initial conditions, diverse objects, and changing objectives. Learning based approaches have shown promise in producing robust policies, but require heavy supervision to efficiently learn precise control, especially from visual inputs. We propose a novel self-supervision technique that uses time-reversal to learn goals and provide a high level plan to reach them. In particular, we introduce the time-reversal model (TRM), a self-supervised model which explores outward from a set of goal states and learns to predict these trajectories in reverse. This provides a high level plan towards goals, allowing us to learn complex manipulation tasks with no demonstrations or exploration at test time. We test our method on the domain of assembly, specifically the mating of tetris-style block pairs. Using our method operating atop visual model predictive control, we are able to assemble tetris blocks on a physical robot using only uncalibrated RGB camera input, and generalize to unseen block pairs. sites.google.com/view/time-reversal
Predicate learning in neural systems: Discovering latent generative structures
Authors: Andrea E. Martin, Leonidas A. A. Doumas
Date: 2018-10-02 00:00:00
Tasks: Distributed Computing

Humans learn complex latent structures from their environments (e.g., natural
language, mathematics, music, social hierarchies). In cognitive science and
cognitive neuroscience, models that infer higher-order structures from sensory
or first-order representations have been proposed to account for the complexity
and flexibility of human behavior. But how do the structures that these models
invoke arise in neural systems in the first place? To answer this question, we
explain how a system can learn latent representational structures (i.e.,
predicates) from experience with wholly unstructured data. During the process
of predicate learning, an artificial neural network exploits the naturally
occurring dynamic properties of distributed computing across neuronal
assemblies in order to learn predicates, but also to combine them
compositionally, two computational aspects which appear to be necessary for
human behavior as per formal theories in multiple domains. We describe how
predicates can be combined generatively using neural oscillations to achieve
human-like extrapolation and compositionality in an artificial neural network.
The ability to learn predicates from experience, to represent structures
compositionally, and to extrapolate to unseen data offers an inroads to
understanding and modeling the most complex human behaviors.