pwc-archive/papers-with-abstracts huggingface dataset:
MUSEFood: Multi-sensor-based Food Volume Estimation on Smartphones
Authors: Junyi Gao, Weihao Tan, Liantao Ma, Yasha Wang, Wen Tang
Date: 2019-03-18 00:00:00
Tasks: Management, Multi-Task Learning, Nutrition

Researches have shown that diet recording can help people increase awareness of food intake and improve nutrition management, and thereby maintain a healthier life. Recently, researchers have been working on smartphone-based diet recording methods and applications that help users accomplish two tasks: record what they eat and how much they eat. Although the former task has made great progress through adopting image recognition technology, it is still a challenge to estimate the volume of foods accurately and conveniently. In this paper, we propose a novel method, named MUSEFood, for food volume estimation. MUSEFood uses the camera to capture photos of the food, but unlike existing volume measurement methods, MUSEFood requires neither training images with volume information nor placing a reference object of known size while taking photos. In addition, considering the impact of different containers on the contour shape of foods, MUSEFood uses a multi-task learning framework to improve the accuracy of food segmentation, and uses a differential model applicable for various containers to further reduce the negative impact of container differences on volume estimation accuracy. Furthermore, MUSEFood uses the microphone and the speaker to accurately measure the vertical distance from the camera to the food in a noisy environment, thus scaling the size of food in the image to its actual size. The experiments on real foods indicate that MUSEFood outperforms state-of-the-art approaches, and highly improves the speed of food volume estimation.
The emergence of number and syntax units in LSTM language models
Authors: Yair Lakretz, German Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, Marco Baroni
Date: 2019-03-18 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling

Recent work has shown that LSTMs trained on a generic language modeling
objective capture syntax-sensitive generalizations such as long-distance number
agreement. We have however no mechanistic understanding of how they accomplish
this remarkable feat. Some have conjectured it depends on heuristics that do
not truly take hierarchical structure into account. We present here a detailed
study of the inner mechanics of number tracking in LSTMs at the single neuron
level. We discover that long-distance number information is largely managed by
two `number units'. Importantly, the behaviour of these units is partially
controlled by other units independently shown to track syntactic structure. We
conclude that LSTMs are, to some extent, implementing genuinely syntactic
processing mechanisms, paving the way to a more general understanding of
grammatical encoding in LSTMs.
Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
Authors: Yang Chen, Xiaoyan Sun, Yaochu Jin
Date: 2019-03-18 00:00:00
Tasks: Federated Learning

Federated learning obtains a central model on the server by aggregating
models trained locally on clients. As a result, federated learning does not
require clients to upload their data to the server, thereby preserving the data
privacy of the clients. One challenge in federated learning is to reduce the
client-server communication since the end devices typically have very limited
communication bandwidth. This paper presents an enhanced federated learning
technique by proposing a synchronous learning strategy on the clients and a
temporally weighted aggregation of the local models on the server. In the
asynchronous learning strategy, different layers of the deep neural networks
are categorized into shallow and deeps layers and the parameters of the deep
layers are updated less frequently than those of the shallow layers.
Furthermore, a temporally weighted aggregation strategy is introduced on the
server to make use of the previously trained local models, thereby enhancing
the accuracy and convergence of the central model. The proposed algorithm is
empirically on two datasets with different deep neural networks. Our results
demonstrate that the proposed asynchronous federated deep learning outperforms
the baseline algorithm both in terms of communication cost and model accuracy.
Neutron: An Implementation of the Transformer Translation Model and its Variants
Authors: Hongfei Xu, Qiuhui Liu
Date: 2019-03-18 00:00:00
Tasks: Translation

The Transformer translation model is easier to parallelize and provides better performance compared to recurrent seq2seq models, which makes it popular among industry and research community. We implement the Neutron in this work, including the Transformer model and its several variants from most recent researches. It is highly optimized, easy to modify and provides comparable performance with interesting features while keeping readability.
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
Authors: Jingwei Zhang, Niklas Wetzel, Nicolai Dorka, Joschka Boedecker, Wolfram Burgard
Date: 2019-03-18 00:00:00
Tasks: Reinforcement Learning

Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration. Commonly these signals are added as bonus rewards, which results in a mixture policy that neither conducts exploration nor task fulfillment resolutely. In this paper, we instead learn separate intrinsic and extrinsic task policies and schedule between these different drives to accelerate exploration and stabilize learning. Moreover, we introduce a new type of intrinsic reward denoted as successor feature control (SFC), which is general and not task-specific. It takes into account statistics over complete trajectories and thus differs from previous methods that only use local information to evaluate intrinsic motivation. We evaluate our proposed scheduled intrinsic drive (SID) agent using three different environments with pure visual inputs: VizDoom, DeepMind Lab and DeepMind Control Suite. The results show a substantially improved exploration efficiency with SFC and the hierarchical usage of the intrinsic drives. A video of our experimental results can be found at https://youtu.be/b0MbY3lUlEI.
Probabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter
Authors: Jorge Ángel González Ordiano, Lutz Gröll, Ralf Mikut, Veit Hagenmeyer
Date: 2019-03-18 00:00:00

Parametric quantile regressions are a useful tool for creating probabilistic
energy forecasts. Nonetheless, since classical quantile regressions are trained
using a non-differentiable cost function, their creation using complex data
mining techniques (e.g., artificial neural networks) may be complicated. This
article presents a method that uses a new nearest neighbors quantile filter to
obtain quantile regressions independently of the utilized data mining technique
and without the non-differentiable cost function. Thereafter, a validation of
the presented method using the dataset of the Global Energy Forecasting
Competition of 2014 is undertaken. The results show that the presented method
is able to solve the competition's task with a similar accuracy and in a
similar time as the competition's winner, but requiring a much less powerful
computer. This property may be relevant in an online forecasting service for
which the fast computation of probabilistic forecasts using not so powerful
machines is required.
On-line learning dynamics of ReLU neural networks using statistical physics techniques
Authors: Michiel Straat, Michael Biehl
Date: 2019-03-18 00:00:00

We introduce exact macroscopic on-line learning dynamics of two-layer neural
networks with ReLU units in the form of a system of differential equations,
using techniques borrowed from statistical physics. For the first experiments,
numerical solutions reveal similar behavior compared to sigmoidal activation
researched in earlier work. In these experiments the theoretical results show
good correspondence with simulations. In ove-rrealizable and unrealizable
learning scenarios, the learning behavior of ReLU networks shows distinctive
characteristics compared to sigmoidal networks.
An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain
Authors: Yijiang Lian, Zhijie Chen, Jinlong Hu, Kefeng Zhang, Chunwei Yan, Muchenxuan Tong, Wenying Han, Hanju Guan, Ying Li, Ying Cao, Yang Yu, Zhigang Li, Xiaochun Liu, Yue Wang
Date: 2019-02-02 00:00:00
Tasks: CPU, Decoder, Machine Translation, NMT, Retrieval, Translation

In this paper, we present a generative retrieval method for sponsored search
engine, which uses neural machine translation (NMT) to generate keywords
directly from query. This method is completely end-to-end, which skips query
rewriting and relevance judging phases in traditional retrieval systems.
Different from standard machine translation, the target space in the retrieval
setting is a constrained closed set, where only committed keywords should be
generated. We present a Trie-based pruning technique in beam search to address
this problem. The biggest challenge in deploying this method into a real
industrial environment is the latency impact of running the decoder.
Self-normalized training coupled with Trie-based dynamic pruning dramatically
reduces the inference time, yielding a speedup of more than 20 times. We also
devise an mixed online-offline serving architecture to reduce the latency and
CPU consumption. To encourage the NMT to generate new keywords uncovered by the
existing system, training data is carefully selected. This model has been
successfully applied in Baidu's commercial search engine as a supplementary
retrieval branch, which has brought a remarkable revenue improvement of more
than 10 percents.
Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition
Authors: Johannes Michael, Roger Labahn, Tobias Grüning, Jochen Zöllner
Date: 2019-03-18 00:00:00
Tasks: Decoder, Handwritten Text Recognition, Image Captioning, Keyword Spotting, Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Translation

Encoder-decoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose an attention-based sequence-to-sequence model. It combines a convolutional neural network as a generic feature extractor with a recurrent neural network to encode both the visual information, as well as the temporal context between characters in the input image, and uses a separate recurrent neural network to decode the actual character sequence. We make experimental comparisons between various attention mechanisms and positional encodings, in order to find an appropriate alignment between the input and output sequence. The model can be trained end-to-end and the optional integration of a hybrid loss allows the encoder to retain an interpretable and usable output, if desired. We achieve competitive results on the IAM and ICFHR2016 READ data sets compared to the state-of-the-art without the use of a language model, and we significantly improve over any recent sequence-to-sequence approaches.
Variance reduction for additive functional of Markov chains via martingale representations
Authors: D. Belomestny, E. Moulines, S. Samsonov
Date: 2019-03-18 00:00:00

In this paper we propose an efficient variance reduction approach for additive functionals of Markov chains relying on a novel discrete time martingale representation. Our approach is fully non-asymptotic and does not require the knowledge of the stationary distribution (and even any type of ergodicity) or specific structure of the underlying density. By rigorously analyzing the convergence properties of the proposed algorithm, we show that its cost-to-variance product is indeed smaller than one of the naive algorithm. The numerical performance of the new method is illustrated for the Langevin-type Markov Chain Monte Carlo (MCMC) methods.
IvaNet: Learning to jointly detect and segment objets with the help of Local Top-Down Modules
Authors: Shihua Huang, Lu Wang
Date: 2019-03-18 00:00:00
Tasks: object-detection, Object Detection, Semantic Segmentation

Driven by Convolutional Neural Networks, object detection and semantic
segmentation have gained significant improvements. However, existing methods on
the basis of a full top-down module have limited robustness in handling those
two tasks simultaneously. To this end, we present a joint multi-task framework,
termed IvaNet. Different from existing methods, our IvaNet backwards abstract
semantic information from higher layers to augment lower layers using local
top-down modules. The comparisons against some counterparts on the PASCAL VOC
and MS COCO datasets demonstrate the functionality of IvaNet.
Clonability of anti-counterfeiting printable graphical codes: a machine learning approach
Authors: Olga Taran, Slavi Bonev, Slava Voloshynovskiy
Date: 2019-03-18 00:00:00
Tasks: BIG-bench Machine Learning

In recent years, printable graphical codes have attracted a lot of attention
enabling a link between the physical and digital worlds, which is of great
interest for the IoT and brand protection applications. The security of
printable codes in terms of their reproducibility by unauthorized parties or
clonability is largely unexplored. In this paper, we try to investigate the
clonability of printable graphical codes from a machine learning perspective.
The proposed framework is based on a simple system composed of fully connected
neural network layers. The results obtained on real codes printed by several
printers demonstrate a possibility to accurately estimate digital codes from
their printed counterparts in certain cases. This provides a new insight on
scenarios, where printable graphical codes can be accurately cloned.
Representing ill-known parts of a numerical model using a machine learning approach
Authors: Julien Brajard, Anastase Charantonis, Jérôme Sirven
Date: 2019-03-18 00:00:00
Tasks: BIG-bench Machine Learning

In numerical modeling of the Earth System, many processes remain unknown or
ill represented (let us quote sub-grid processes, the dependence to unknown
latent variables or the non-inclusion of complex dynamics in numerical models)
but sometimes can be observed. This paper proposes a methodology to produce a
hybrid model combining a physical-based model (forecasting the well-known
processes) with a neural-net model trained from observations (forecasting the
remaining processes). The approach is applied to a shallow-water model in which
the forcing, dissipative and diffusive terms are assumed to be unknown. We show
that the hybrid model is able to reproduce with great accuracy the unknown
terms (correlation close to 1). For long term simulations it reproduces with no
significant difference the mean state, the kinetic energy, the potential energy
and the potential vorticity of the system. Lastly it is able to function with
new forcings that were not encountered during the training phase of the neural
network.
On Deep Set Learning and the Choice of Aggregations
Authors: Maximilian Soelch, Adnan Akhundov, Patrick van der Smagt, Justin Bayer
Date: 2019-03-18 00:00:00
Tasks: Sensitivity

Recently, it has been shown that many functions on sets can be represented by sum decompositions. These decompositons easily lend themselves to neural approximations, extending the applicability of neural nets to set-valued inputs---Deep Set learning. This work investigates a core component of Deep Set architecture: aggregation functions. We suggest and examine alternatives to commonly used aggregation functions, including learnable recurrent aggregation functions. Empirically, we show that the Deep Set networks are highly sensitive to the choice of aggregation functions: beyond improved performance, we find that learnable aggregations lower hyper-parameter sensitivity and generalize better to out-of-distribution input size.
Deep Gaussian Processes for Multi-fidelity Modeling
Authors: Kurt Cutajar, Mark Pullin, Andreas Damianou, Neil Lawrence, Javier González
Date: 2019-03-18 00:00:00
Tasks: Bayesian Optimization, Decision Making, Gaussian Processes, Variational Inference

Multi-fidelity methods are prominently used when cheaply-obtained, but
possibly biased and noisy, observations must be effectively combined with
limited or expensive true data in order to construct reliable models. This
arises in both fundamental machine learning procedures such as Bayesian
optimization, as well as more practical science and engineering applications.
In this paper we develop a novel multi-fidelity model which treats layers of a
deep Gaussian process as fidelity levels, and uses a variational inference
scheme to propagate uncertainty across them. This allows for capturing
nonlinear correlations between fidelities with lower risk of overfitting than
existing methods exploiting compositional structure, which are conversely
burdened by structural assumptions and constraints. We show that the proposed
approach makes substantial improvements in quantifying and propagating
uncertainty in multi-fidelity set-ups, which in turn improves their
effectiveness in decision making pipelines.
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
Authors: Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael R. Lyu, Irwin King
Date: 2019-03-18 00:00:00
Proceeding: TACL 2019 3
Tasks: General Classification

This paper presents an unsupervised framework for jointly modeling topic
content and discourse behavior in microblog conversations. Concretely, we
propose a neural model to discover word clusters indicating what a conversation
concerns (i.e., topics) and those reflecting how participants voice their
opinions (i.e., discourse). Extensive experiments show that our model can yield
both coherent topics and meaningful discourse behavior. Further study shows
that our topic and discourse representations can benefit the classification of
microblog messages, especially when they are jointly trained with the
classifier.
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction
Authors: Alex Wong, Byung-Woo Hong, Stefano Soatto
Date: 2019-03-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Depth Estimation, Depth Prediction

Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth. We follow a geometric approach that exploits abundant stereo imagery to learn a model to hypothesize scene structure without direct supervision. Although we train a network with stereo pairs, we only require a single image at test time to hypothesize disparity or depth. We propose a novel objective function that exploits the bilateral cyclic relationship between the left and right disparities and we introduce an adaptive regularization scheme that allows the network to handle both the co-visible and occluded regions in a stereo pair. This process ultimately produces a model to generate hypotheses for the 3-dimensional structure of the scene as viewed in a single image. When used to generate a single (most probable) estimate of depth, our method outperforms state-of-the-art unsupervised monocular depth prediction methods on the KITTI benchmarks. We show that our method generalizes well by applying our models trained on KITTI to the Make3d dataset.
Low-rank approximations of hyperbolic embeddings
Authors: Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra
Date: 2019-03-18 00:00:00

The hyperbolic manifold is a smooth manifold of negative constant curvature.
While the hyperbolic manifold is well-studied in the literature, it has gained
interest in the machine learning and natural language processing communities
lately due to its usefulness in modeling continuous hierarchies. Tasks with
hierarchical structures are ubiquitous in those fields and there is a general
interest to learning hyperbolic representations or embeddings of such tasks.
Additionally, these embeddings of related tasks may also share a low-rank
subspace. In this work, we propose to learn hyperbolic embeddings such that
they also lie in a low-dimensional subspace. In particular, we consider the
problem of learning a low-rank factorization of hyperbolic embeddings. We cast
these problems as manifold optimization problems and propose computationally
efficient algorithms. Empirical results illustrate the efficacy of the proposed
approach.
M$^2$VAE - Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood
Authors: Timo Korthals
Date: 2019-03-18 00:00:00

This work gives an in-depth derivation of the trainable evidence lower bound
obtained from the marginal joint log-Likelihood with the goal of training a
Multi-Modal Variational Autoencoder (M$^2$VAE).
Autoregressive Models for Sequences of Graphs
Authors: Daniele Zambon, Daniele Grattarola, Lorenzo Livi, Cesare Alippi
Date: 2019-03-18 00:00:00
Tasks: Graph Neural Network

This paper proposes an autoregressive (AR) model for sequences of graphs,
which generalises traditional AR models. A first novelty consists in
formalising the AR model for a very general family of graphs, characterised by
a variable topology, and attributes associated with nodes and edges. A graph
neural network (GNN) is also proposed to learn the AR function associated with
the graph-generating process (GGP), and subsequently predict the next graph in
a sequence. The proposed method is compared with four baselines on synthetic
GGPs, denoting a significantly better performance on all considered problems.
Appearance-Based Gaze Estimation Using Dilated-Convolutions
Authors: Zhaokang Chen, Bertram E. Shi
Date: 2019-03-18 00:00:00
Tasks: Contact Detection, Gaze Estimation

Appearance-based gaze estimation has attracted more and more attention
because of its wide range of applications. The use of deep convolutional neural
networks has improved the accuracy significantly. In order to improve the
estimation accuracy further, we focus on extracting better features from eye
images. Relatively large changes in gaze angles may result in relatively small
changes in eye appearance. We argue that current architectures for gaze
estimation may not be able to capture such small changes, as they apply
multiple pooling layers or other downsampling layers so that the spatial
resolution of the high-level layers is reduced significantly. To evaluate
whether the use of features extracted at high resolution can benefit gaze
estimation, we adopt dilated-convolutions to extract high-level features
without reducing spatial resolution. In cross-subject experiments on the
Columbia Gaze dataset for eye contact detection and the MPIIGaze dataset for 3D
gaze vector regression, the resulting Dilated-Nets achieve significant (up to
20.8%) gains when compared to similar networks without dilated-convolutions.
Our proposed Dilated-Net achieves state-of-the-art results on both the Columbia
Gaze and the MPIIGaze datasets.