pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Synthetic Gradients and Decoupled Neural Interfaces
Authors: Wojciech Marian Czarnecki, Grzegorz Świrszcz, Max Jaderberg, Simon Osindero, Oriol Vinyals, Koray Kavukcuoglu
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8

When training neural networks, the use of Synthetic Gradients (SG) allows
layers or modules to be trained without update locking - without waiting for a
true error gradient to be backpropagated - resulting in Decoupled Neural
Interfaces (DNIs). This unlocked ability of being able to update parts of a
neural network asynchronously and with only local information was demonstrated
to work empirically in Jaderberg et al (2016). However, there has been very
little demonstration of what changes DNIs and SGs impose from a functional,
representational, and learning dynamics point of view. In this paper, we study
DNIs through the use of synthetic gradients on feed-forward networks to better
understand their behaviour and elucidate their effect on optimisation. We show
that the incorporation of SGs does not affect the representational strength of
the learning system for a neural network, and prove the convergence of the
learning system for linear and deep linear models. On practical problems we
investigate the mechanism by which synthetic gradient estimators approximate
the true loss, and, surprisingly, how that leads to drastically different
layer-wise representations. Finally, we also expose the relationship of using
synthetic gradients to other error approximation techniques and find a unifying
language for discussion and comparison.
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Authors: Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore
Date: 2017-03-01 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, Diversity

The selection, development, or comparison of machine learning methods in data
mining can be a difficult task based on the target problem and goals of a
particular study. Numerous publicly available real-world and simulated
benchmark datasets have emerged from different sources, but their organization
and adoption as standards have been inconsistent. As such, selecting and
curating specific benchmarks remains an unnecessary burden on machine learning
practitioners and data scientists. The present study introduces an accessible,
curated, and developing public benchmark resource to facilitate identification
of the strengths and weaknesses of different machine learning methodologies. We
compare meta-features among the current set of benchmark datasets in this
resource to characterize the diversity of available data. Finally, we apply a
number of established machine learning methods to the entire benchmark suite
and analyze how datasets and algorithms cluster in terms of performance. This
work is an important first step towards understanding the limitations of
popular benchmarking suites and developing a resource that connects existing
benchmarking standards to more diverse and efficient standards in the future.
Memory vectors for similarity search in high-dimensional spaces
Authors: Ahmet Iscen, Teddy Furon, Vincent Gripon, Michael Rabbat, Hervé Jégou
Date: 2014-12-10 00:00:00
Tasks: Image Retrieval, Vocal Bursts Intensity Prediction

We study an indexing architecture to store and search in a database of
high-dimensional vectors from the perspective of statistical signal processing
and decision theory. This architecture is composed of several memory units,
each of which summarizes a fraction of the database by a single representative
vector. The potential similarity of the query to one of the vectors stored in
the memory unit is gauged by a simple correlation with the memory unit's
representative vector. This representative optimizes the test of the following
hypothesis: the query is independent from any vector in the memory unit vs. the
query is a simple perturbation of one of the stored vectors.
  Compared to exhaustive search, our approach finds the most similar database
vectors significantly faster without a noticeable reduction in search quality.
Interestingly, the reduction of complexity is provably better in
high-dimensional spaces. We empirically demonstrate its practical interest in a
large-scale image search scenario with off-the-shelf state-of-the-art
descriptors.
Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions
Authors: Tianmin Shu, Xiaofeng Gao, Michael S. Ryoo, Song-Chun Zhu
Date: 2017-03-01 00:00:00

In this paper, we present a general framework for learning social affordance
grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human
interactions, and transfer the grammar to humanoids to enable a real-time
motion inference for human-robot interaction (HRI). Based on Gibbs sampling,
our weakly supervised grammar learning can automatically construct a
hierarchical representation of an interaction with long-term joint sub-tasks of
both agents and short term atomic actions of individual agents. Based on a new
RGB-D video dataset with rich instances of human interactions, our experiments
of Baxter simulation, human evaluation, and real Baxter test demonstrate that
the model learned from limited training data successfully generates human-like
behaviors in unseen scenarios and outperforms both baselines.
Balancing New Against Old Information: The Role of Surprise in Learning
Authors: Mohammadjavad Faraji, Kerstin Preuschoff, Wulfram Gerstner
Date: 2016-06-17 00:00:00
Tasks: Decision Making

Surprise describes a range of phenomena from unexpected events to behavioral
responses. We propose a measure of surprise and use it for surprise-driven
learning. Our surprise measure takes into account data likelihood as well as
the degree of commitment to a belief via the entropy of the belief
distribution. We find that surprise-minimizing learning dynamically adjusts the
balance between new and old information without the need of knowledge about the
temporal statistics of the environment. We apply our framework to a dynamic
decision-making task and a maze exploration task. Our surprise minimizing
framework is suitable for learning in complex environments, even if the
environment undergoes gradual or sudden changes and could eventually provide a
framework to study the behavior of humans and animals encountering surprising
events.
Truth and Regret in Online Scheduling
Authors: Shuchi Chawla, Nikhil Devanur, Janardhan Kulkarni, Rad Niazadeh
Date: 2017-03-01 00:00:00
Tasks: Scheduling

We consider a scheduling problem where a cloud service provider has multiple
units of a resource available over time. Selfish clients submit jobs, each with
an arrival time, deadline, length, and value. The service provider's goal is to
implement a truthful online mechanism for scheduling jobs so as to maximize the
social welfare of the schedule. Recent work shows that under a stochastic
assumption on job arrivals, there is a single-parameter family of mechanisms
that achieves near-optimal social welfare. We show that given any such family
of near-optimal online mechanisms, there exists an online mechanism that in the
worst case performs nearly as well as the best of the given mechanisms. Our
mechanism is truthful whenever the mechanisms in the given family are truthful
and prompt, and achieves optimal (within constant factors) regret.
  We model the problem of competing against a family of online scheduling
mechanisms as one of learning from expert advice. A primary challenge is that
any scheduling decisions we make affect not only the payoff at the current
step, but also the resource availability and payoffs in future steps.
Furthermore, switching from one algorithm (a.k.a. expert) to another in an
online fashion is challenging both because it requires synchronization with the
state of the latter algorithm as well as because it affects the incentive
structure of the algorithms. We further show how to adapt our algorithm to a
non-clairvoyant setting where job lengths are unknown until jobs are run to
completion. Once again, in this setting, we obtain truthfulness along with
asymptotically optimal regret (within poly-logarithmic factors).
Reinforcement Learning for Pivoting Task
Authors: Rika Antonova, Silvia Cruciani, Christian Smith, Danica Kragic
Date: 2017-03-01 00:00:00
Tasks: continuous-control, Continuous Control, Friction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work we propose an approach to learn a robust policy for solving the
pivoting task. Recently, several model-free continuous control algorithms were
shown to learn successful policies without prior knowledge of the dynamics of
the task. However, obtaining successful policies required thousands to millions
of training episodes, limiting the applicability of these approaches to real
hardware. We developed a training procedure that allows us to use a simple
custom simulator to learn policies robust to the mismatch of simulation vs
robot. In our experiments, we demonstrate that the policy learned in the
simulator is able to pivot the object to the desired target angle on the real
robot. We also show generalization to an object with different inertia, shape,
mass and friction properties than those used during training. This result is a
step towards making model-free reinforcement learning available for solving
robotics tasks via pre-training in simulators that offer only an imprecise
match to the real-world dynamics.
Unsupervised Domain Adaptation Using Approximate Label Matching
Authors: Jordan T. Ash, Robert E. Schapire, Barbara E. Engelhardt
Date: 2016-02-16 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Domain adaptation addresses the problem created when training data is
generated by a so-called source distribution, but test data is generated by a
significantly different target distribution. In this work, we present
approximate label matching (ALM), a new unsupervised domain adaptation
technique that creates and leverages a rough labeling on the test samples, then
uses these noisy labels to learn a transformation that aligns the source and
target samples. We show that the transformation estimated by ALM has favorable
properties compared to transformations estimated by other methods, which do not
use any kind of target labeling. Our model is regularized by requiring that a
classifier trained to discriminate source from transformed target samples
cannot distinguish between the two. We experiment with ALM on simulated and
real data, and show that it outperforms techniques commonly used in the field.
HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving
Authors: Cezary Kaliszyk, François Chollet, Christian Szegedy
Date: 2017-03-01 00:00:00
Tasks: Automated Theorem Proving, BIG-bench Machine Learning

Large computer-understandable proofs consist of millions of intermediate
logical steps. The vast majority of such steps originate from manually selected
and manually guided heuristics applied to intermediate goals. So far, machine
learning has generally not been used to filter or generate these steps. In this
paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for
the purpose of developing new machine learning-based theorem-proving
strategies. We make this dataset publicly available under the BSD license. We
propose various machine learning tasks that can be performed on this dataset,
and discuss their significance for theorem proving. We also benchmark a set of
simple baseline machine learning models suited for the tasks (including
logistic regression, convolutional neural networks and recurrent neural
networks). The results of our baseline models show the promise of applying
machine learning to HOL theorem proving.
Augmented Reality for Depth Cues in Monocular Minimally Invasive Surgery
Authors: Long Chen, Wen Tang, Nigel W. John, Tao Ruan Wan, Jian Jun Zhang
Date: 2017-03-01 00:00:00
Tasks: Simultaneous Localization and Mapping, Surface Reconstruction

One of the major challenges in Minimally Invasive Surgery (MIS) such as
laparoscopy is the lack of depth perception. In recent years, laparoscopic
scene tracking and surface reconstruction has been a focus of investigation to
provide rich additional information to aid the surgical process and compensate
for the depth perception issue. However, robust 3D surface reconstruction and
augmented reality with depth perception on the reconstructed scene are yet to
be reported. This paper presents our work in this area. First, we adopt a
state-of-the-art visual simultaneous localization and mapping (SLAM) framework
- ORB-SLAM - and extend the algorithm for use in MIS scenes for reliable
endoscopic camera tracking and salient point mapping. We then develop a robust
global 3D surface reconstruction frame- work based on the sparse point clouds
extracted from the SLAM framework. Our approach is to combine an outlier
removal filter within a Moving Least Squares smoothing algorithm and then
employ Poisson surface reconstruction to obtain smooth surfaces from the
unstructured sparse point cloud. Our proposed method has been quantitatively
evaluated compared with ground-truth camera trajectories and the organ model
surface we used to render the synthetic simulation videos. In vivo laparoscopic
videos used in the tests have demonstrated the robustness and accuracy of our
proposed framework on both camera tracking and surface reconstruction,
illustrating the potential of our algorithm for depth augmentation and
depth-corrected augmented reality in MIS with monocular endoscopes.
Lossy Image Compression with Compressive Autoencoders
Authors: Lucas Theis, Wenzhe Shi, Andrew Cunningham, Ferenc Huszár
Date: 2017-03-01 00:00:00
Tasks: Image Compression

We propose a new approach to the problem of optimizing autoencoders for lossy
image compression. New media formats, changing hardware technology, as well as
diverse requirements and content types create a need for compression algorithms
which are more flexible than existing codecs. Autoencoders have the potential
to address this need, but are difficult to optimize directly due to the
inherent non-differentiabilty of the compression loss. We here show that
minimal changes to the loss are sufficient to train deep autoencoders
competitive with JPEG 2000 and outperforming recently proposed approaches based
on RNNs. Our network is furthermore computationally efficient thanks to a
sub-pixel architecture, which makes it suitable for high-resolution images.
This is in contrast to previous work on autoencoders for compression using
coarser approximations, shallower architectures, computationally expensive
methods, or focusing on small images.
Multiple Hypothesis Colorization
Authors: Mohammad Haris Baig, Lorenzo Torresani
Date: 2016-06-20 00:00:00
Tasks: Colorization, Image Compression

In this work we focus on the problem of colorization for image compression.
Since color information occupies a large proportion of the total storage size
of an image, a method that can predict accurate color from its grayscale
version can produce dramatic reduction in image file size. But colorization for
compression poses several challenges. First, while colorization for artistic
purposes simply involves predicting plausible chroma, colorization for
compression requires generating output colors that are as close as possible to
the ground truth. Second, many objects in the real world exhibit multiple
possible colors. Thus, to disambiguate the colorization problem some additional
information must be stored to reproduce the true colors with good accuracy. To
account for the multimodal color distribution of objects we propose a deep
tree-structured network that generates multiple color hypotheses for every
pixel from a grayscale picture (as opposed to a single color produced by most
prior colorization approaches). We show how to leverage the multimodal output
of our model to reproduce with high fidelity the true colors of an image by
storing very little additional information. In the experiments we show that our
proposed method outperforms traditional JPEG color coding by a large margin,
producing colors that are nearly indistinguishable from the ground truth at the
storage cost of just a few hundred bytes for high-resolution pictures!
The Statistical Recurrent Unit
Authors: Junier B. Oliva, Barnabas Poczos, Jeff Schneider
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8
Tasks: Bayesian Optimization

Sophisticated gated recurrent neural network architectures like LSTMs and
GRUs have been shown to be highly effective in a myriad of applications. We
develop an un-gated unit, the statistical recurrent unit (SRU), that is able to
learn long term dependencies in data by only keeping moving averages of
statistics. The SRU's architecture is simple, un-gated, and contains a
comparable number of parameters to LSTMs; yet, SRUs perform favorably to more
sophisticated LSTM and GRU alternatives, often outperforming one or both in
various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an
unbiased manner by optimizing respective architectures' hyperparameters in a
Bayesian optimization scheme for both synthetic and real-world tasks.
Gradient Boosting on Stochastic Data Streams
Authors: Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, J. Andrew Bagnell
Date: 2017-03-01 00:00:00

Boosting is a popular ensemble algorithm that generates more powerful
learners by linearly combining base models from a simpler hypothesis class. In
this work, we investigate the problem of adapting batch gradient boosting for
minimizing convex loss functions to online setting where the loss at each
iteration is i.i.d sampled from an unknown distribution. To generalize from
batch to online, we first introduce the definition of online weak learning edge
with which for strongly convex and smooth loss functions, we present an
algorithm, Streaming Gradient Boosting (SGB) with exponential shrinkage
guarantees in the number of weak learners. We further present an adaptation of
SGB to optimize non-smooth loss functions, for which we derive a O(ln N/N)
convergence rate. We also show that our analysis can extend to adversarial
online learning setting under a stronger assumption that the online weak
learning edge will hold in adversarial setting. We finally demonstrate
experimental results showing that in practice our algorithms can achieve
competitive results as classic gradient boosting while using less computation.
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
Authors: Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause
Date: 2016-06-17 00:00:00
Tasks: Data Summarization, energy management, Management

Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation guarantees. Specifically, i) We introduce the weak DR property that gives a unified characterization of submodularity for all set, integer-lattice and continuous functions; ii) for maximizing monotone DR-submodular continuous functions under general down-closed convex constraints, we propose a Frank-Wolfe variant with $(1-1/e)$ approximation guarantee, and sub-linear convergence rate; iii) for maximizing general non-monotone submodular continuous functions subject to box constraints, we propose a DoubleGreedy algorithm with $1/3$ approximation guarantee. Submodular continuous functions naturally find applications in various real-world settings, including influence and revenue maximization with continuous assignments, sensor energy management, multi-resolution data summarization, facility location, etc. Experimental results show that the proposed algorithms efficiently generate superior solutions compared to baseline algorithms.
A Probabilistic Framework for Location Inference from Social Media
Authors: Yujie Qian, Jie Tang, Zhilin Yang, Binxuan Huang, Wei Wei, Kathleen M. Carley
Date: 2017-02-23 00:00:00
Tasks: Management

We study the extent to which we can infer users' geographical locations from social media. Location inference from social media can benefit many applications, such as disaster management, targeted advertising, and news content tailoring. The challenges, however, lie in the limited amount of labeled data and the large scale of social networks. In this paper, we formalize the problem of inferring location from social media into a semi-supervised factor graph model (SSFGM). The model provides a probabilistic framework in which various sources of information (e.g., content and social network) can be combined together. We design a two-layer neural network to learn feature representations, and incorporate the learned latent features into SSFGM. To deal with the large-scale problem, we propose a Two-Chain Sampling (TCS) algorithm to learn SSFGM. The algorithm achieves a good trade-off between accuracy and efficiency. Experiments on Twitter and Weibo show that the proposed TCS algorithm for SSFGM can substantially improve the inference accuracy over several state-of-the-art methods. More importantly, TCS achieves over 100x speedup comparing with traditional propagation-based methods (e.g., loopy belief propagation).
Flexible constrained sampling with guarantees for pattern mining
Authors: Vladimir Dzyuba, Matthijs van Leeuwen, Luc De Raedt
Date: 2016-10-28 00:00:00

Pattern sampling has been proposed as a potential solution to the infamous
pattern explosion. Instead of enumerating all patterns that satisfy the
constraints, individual patterns are sampled proportional to a given quality
measure. Several sampling algorithms have been proposed, but each of them has
its limitations when it comes to 1) flexibility in terms of quality measures
and constraints that can be used, and/or 2) guarantees with respect to sampling
accuracy. We therefore present Flexics, the first flexible pattern sampler that
supports a broad class of quality measures and constraints, while providing
strong guarantees regarding sampling accuracy. To achieve this, we leverage the
perspective on pattern mining as a constraint satisfaction problem and build
upon the latest advances in sampling solutions in SAT as well as existing
pattern mining algorithms. Furthermore, the proposed algorithm is applicable to
a variety of pattern languages, which allows us to introduce and tackle the
novel task of sampling sets of patterns. We introduce and empirically evaluate
two variants of Flexics: 1) a generic variant that addresses the well-known
itemset sampling task and the novel pattern set sampling task as well as a wide
range of expressive constraints within these tasks, and 2) a specialized
variant that exploits existing frequent itemset techniques to achieve
substantial speed-ups. Experiments show that Flexics is both accurate and
efficient, making it a useful tool for pattern-based data exploration.