pwc-archive/papers-with-abstracts huggingface dataset:
ANODEV2: A Coupled Neural ODE Evolution Framework
Authors: Tianjun Zhang, Zhewei Yao, Amir Gholami, Kurt Keutzer, Joseph Gonzalez, George Biros, Michael Mahoney
Date: 2019-06-10 00:00:00

It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE). This observation motivated the introduction of so-called Neural ODEs, which allow more general discretization schemes with adaptive time stepping. Here, we propose ANODEV2, which is an extension of this approach that also allows evolution of the neural network parameters, in a coupled ODE-based formulation. The Neural ODE method introduced earlier is in fact a special case of this new more general framework. We present the formulation of ANODEV2, derive optimality conditions, and implement a coupled reaction-diffusion-advection version of this framework in PyTorch. We present empirical results using several different configurations of ANODEV2, testing them on multiple models on CIFAR-10. We report results showing that this coupled ODE-based framework is indeed trainable, and that it achieves higher accuracy, as compared to the baseline models as well as the recently-proposed Neural ODE approach.
Chinese Embedding via Stroke and Glyph Information: A Dual-channel View
Authors: Hanqing Tao, Shiwei Tong, Tong Xu, Qi Liu, Enhong Chen
Date: 2019-06-03 00:00:00
Tasks: Word Embeddings, Word Similarity

Recent studies have consistently given positive hints that morphology is helpful in enriching word embeddings. In this paper, we argue that Chinese word embeddings can be substantially enriched by the morphological information hidden in characters which is reflected not only in strokes order sequentially, but also in character glyphs spatially. Then, we propose a novel Dual-channel Word Embedding (DWE) model to realize the joint learning of sequential and spatial information of characters. Through the evaluation on both word similarity and word analogy tasks, our model shows its rationality and superiority in modelling the morphology of Chinese.
CVPR19 Tracking and Detection Challenge: How crowded can it get?
Authors: Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixe
Date: 2019-06-10 00:00:00
Tasks: Multiple Object Tracking, Multiple People Tracking, Object, Object Tracking

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16 and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios.
An Unsupervised Framework for Comparing Graph Embeddings
Authors: Bogumil Kaminski, Pawel Pralat, Francois Theberge
Date: 2019-05-29 00:00:00
Tasks: Graph Embedding

Graph embedding is a transformation of vertices of a graph into set of vectors. Good embeddings should capture the graph topology, vertex-to-vertex relationship, and other relevant information about graphs, subgraphs, and vertices. If these objectives are achieved, they are meaningful, understandable, and compressed representations of networks. They also provide more options and tools for data scientists as machine learning on graphs is still quite limited. Finally, vector operations are simpler and faster than comparable operations on graphs. The main challenge is that one needs to make sure that embeddings well describe the properties of the graphs. In particular, the decision has to be made on the embedding dimensionality which highly impacts the quality of an embedding. As a result, selecting the best embedding is a challenging task and very often requires domain experts. In this paper, we propose a ``divergence score'' that can be assign to various embeddings to distinguish good ones from bad ones. This general framework provides a tool for an unsupervised graph embedding comparison. In order to achieve it, we needed to generalize the well-known Chung-Lu model to incorporate geometry which is interesting on its own rights. In order to test our framework, we did a number of experiments with synthetic networks as well as real-world networks, and various embedding algorithms.
CNN depth analysis with different channel inputs for Acoustic Scene Classification
Authors: Sergi Perez-Castanos, Javier Naranjo-Alcazar, Pedro Zuccarello, Maximo Cobos, Frances J. Ferri
Date: 2019-06-10 00:00:00
Tasks: Acoustic Scene Classification, Audio Classification, General Classification, image-classification, Image Classification, Scene Classification

Acoustic scene classification (ASC) has been approached in the last years using deep learning techniques such as convolutional neural networks or recurrent neural networks. Many state-of-the-art solutions are based on image classification frameworks and, as such, a 2D representation of the audio signal is considered for training these networks. Finding the most suitable audio representation is still a research area of interest. In this paper, different log-Mel representations and combinations are analyzed. Experiments show that the best results are obtained using the harmonic and percussive components plus the difference between left and right stereo channels, (L-R). On the other hand, it is a common strategy to ensemble different models in order to increase the final accuracy. Even though averaging different model predictions is a common choice, an exhaustive analysis of different ensemble techniques has not been presented in ASC problems. In this paper, geometric and arithmetic mean plus the Ordered Weighted Averaging (OWA) operator are studied as aggregation operators for the output of the different models of the ensemble. Finally, the work carried out in this paper is highly oriented towards real-time implementations. In this context, as the number of applications for audio classification on edge devices is increasing exponentially, we also analyze different network depths and efficient solutions for aggregating ensemble predictions.
Calculating the Optimal Step in Shift-Reduce Dependency Parsing: From Cubic to Linear Time
Authors: Mark-Jan Nederhof
Date: 2019-03-01 00:00:00
Proceeding: TACL 2019 3
Tasks: Dependency Parsing

We present a new cubic-time algorithm to calculate the optimal next step in shift-reduce dependency parsing, relative to ground truth, commonly referred to as dynamic oracle. Unlike existing algorithms, it is applicable if the training corpus contains non-projective structures. We then show that for a projective training corpus, the time complexity can be improved from cubic to linear.
Community Detection with Graph Neural Networks
Authors: Zhengdao Chen, Xiang Li, Joan Bruna
Date: 2018-10-25 00:00:00
Proceeding: ICLR 2018 10
Tasks: Community Detection, Graph Classification, Graph Neural Network, Stochastic Block Model

We study data-driven methods for community detection on graphs, an inverse problem that is typically solved in terms of the spectrum of certain operators or via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational signal-to-noise detection thresholds. This graph inference task can be recast as a node-wise graph classification problem, and, as such, computational detection thresholds can be translated in terms of learning within appropriate models. We present a novel family of Graph Neural Networks (GNNs) and show that they can reach those detection thresholds in a purely data-driven manner without access to the underlying generative models, and even improve upon current computational thresholds in hard regimes. For that purpose, we propose to augment GNNs with the non-backtracking operator, defined on the line graph of edge adjacencies. We also perform the first analysis of optimization landscape on using GNNs to solve community detection problems, demonstrating that under certain simplifications and assumptions, the loss value at the local minima is close to the loss value at the global minimum/minima. Finally, the resulting model is also tested on real datasets, performing significantly better than previous models.
Suppressing Model Overfitting for Image Super-Resolution Networks
Authors: Ruicheng Feng, Jinjin Gu, Yu Qiao, Chao Dong
Date: 2019-06-11 00:00:00
Tasks: Diversity, Image Super-Resolution, Memorization, model, Super-Resolution

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to the limited accessible training pairs, large models exhibit undesirable behaviors such as overfitting and memorization. To suppress model overfitting and further enjoy the merits of large model capacity, we thoroughly investigate generic approaches for supplying additional training data pairs. In particular, we introduce a simple learning principle MixUp to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples. In addition, we propose a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity. This strategy proves to be successful in reducing biases of data. By combining these components -- MixUp and synthetic training data, large models can be trained without overfitting under very limited data samples and achieve satisfactory generalization performance. Our method won the second place in NTIRE2019 Real SR Challenge.
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks
Authors: Thang Vu, Cao V. Nguyen, Trung X. Pham, Tung M. Luu, Chang D. Yoo
Date: 2018-09-01 00:00:00
Proceeding: ECCV2018 2018 9
Tasks: Image Enhancement, Image Super-Resolution, Super-Resolution

This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution
or image enhancement to provide accurate yet visually pleasing images
on mobile devices by addressing the following three main issues. First,
the considered FEQE performs majority of its computation in a lowresolution space. Second, the number of channels used in the convolutional layers is small which allows FEQE to be very deep. Third, the
FEQE performs downsampling referred to as desubpixel that does not
lead to loss of information. Experimental results on a number of standard
benchmark datasets show significant improvements in image fidelity and
reduction in processing time of the proposed FEQE compared to the recent state-of-the-art methods. In the PIRM 2018 challenge, the proposed
FEQE placed first on the image super-resolution task for mobile devices.
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
Authors: Ryan Y. Benmalek, Madian Khabsa, Suma Desu, Claire Cardie, Michele Banko
Date: 2019-06-12 00:00:00
Tasks: Decoder, Machine Translation, Question Generation, Question-Generation, Text Generation, Text Summarization, Translation

We introduce the Scratchpad Mechanism, a novel addition to the sequence-to-sequence (seq2seq) neural network architecture and demonstrate its effectiveness in improving the overall fluency of seq2seq models for natural language generation tasks. By enabling the decoder at each time step to write to all of the encoder output layers, Scratchpad can employ the encoder as a "scratchpad" memory to keep track of what has been generated so far and thereby guide future generation. We evaluate Scratchpad in the context of three well-studied natural language generation tasks --- Machine Translation, Question Generation, and Text Summarization --- and obtain state-of-the-art or comparable performance on standard datasets for each task. Qualitative assessments in the form of human judgements (question generation), attention visualization (MT), and sample output (summarization) provide further evidence of the ability of Scratchpad to generate fluent and expressive output.
Efficient Exploration via State Marginal Matching
Authors: Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, Ruslan Salakhutdinov
Date: 2019-06-12 00:00:00
Tasks: Efficient Exploration, Reinforcement Learning, Unsupervised Reinforcement Learning

Exploration is critical to a reinforcement learning agent's performance in its given environment. Prior exploration methods are often based on using heuristic auxiliary predictions to guide policy behavior, lacking a mathematically-grounded objective with clear properties. In contrast, we recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution. The target distribution is a uniform distribution in most cases, but can incorporate prior knowledge if available. In effect, SMM amortizes the cost of learning to explore in a given environment. The SMM objective can be viewed as a two-player, zero-sum game between a state density model and a parametric policy, an idea that we use to build an algorithm for optimizing the SMM objective. Using this formalism, we further demonstrate that prior work approximately maximizes the SMM objective, offering an explanation for the success of these methods. On both simulated and real-world tasks, we demonstrate that agents that directly optimize the SMM objective explore faster and adapt more quickly to new tasks as compared to prior exploration methods.
Critical Point Finding with Newton-MR by Analogy to Computing Square Roots
Authors: Charles G. Frye
Date: 2019-06-12 00:00:00

Understanding of the behavior of algorithms for resolving the optimization problem (hereafter shortened to OP) of optimizing a differentiable loss function (OP1), is enhanced by knowledge of the critical points of that loss function, i.e. the points where the gradient is 0. Here, we describe a solution to the problem of finding critical points by proposing and solving three optimization problems: 1) minimizing the norm of the gradient (OP2), 2) minimizing the difference between the pre-conditioned update direction and the gradient (OP3), and 3) minimizing the norm of the gradient along the update direction (OP4). The result is a recently-introduced algorithm for optimizing invex functions, Newton-MR, which turns out to be highly effective at the problem of finding the critical points of the loss surfaces of neural networks. We precede this derivation with an analogous, but simpler, derivation of the nested-optimization algorithm for computing square roots by combining Heron's Method with Newton-Raphson division.
Presence-Only Geographical Priors for Fine-Grained Image Classification
Authors: Oisin Mac Aodha, Elijah Cole, Pietro Perona
Date: 2019-06-12 00:00:00
Proceeding: ICCV 2019 10
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification

Appearance information alone is often not sufficient to accurately differentiate between fine-grained visual categories. Human experts make use of additional cues such as where, and when, a given image was taken in order to inform their final decision. This contextual information is readily available in many online image collections but has been underutilized by existing image classifiers that focus solely on making predictions based on the image contents. We propose an efficient spatio-temporal prior, that when conditioned on a geographical location and time, estimates the probability that a given object category occurs at that location. Our prior is trained from presence-only observation data and jointly models object categories, their spatio-temporal distributions, and photographer biases. Experiments performed on multiple challenging image classification datasets show that combining our prior with the predictions from image classifiers results in a large improvement in final classification performance.
Does Learning Require Memorization? A Short Tale about a Long Tail
Authors: Vitaly Feldman
Date: 2019-06-12 00:00:00
Tasks: Memorization, Model Compression

State-of-the-art results on image recognition tasks are achieved using over-parameterized learning algorithms that (nearly) perfectly fit the training set and are known to fit well even random labels. This tendency to memorize the labels of the training data is not explained by existing theoretical analyses. Memorization of the training data also presents significant privacy risks when the training data contains sensitive personal information and thus it is important to understand whether such memorization is necessary for accurate learning. We provide the first conceptual explanation and a theoretical model for this phenomenon. Specifically, we demonstrate that for natural data distributions memorization of labels is necessary for achieving close-to-optimal generalization error. Crucially, even labels of outliers and noisy labels need to be memorized. The model is motivated and supported by the results of several recent empirical works. In our model, data is sampled from a mixture of subpopulations and our results show that memorization is necessary whenever the distribution of subpopulation frequencies is long-tailed. Image and text data is known to be long-tailed and therefore our results establish a formal link between these empirical phenomena. Our results allow to quantify the cost of limiting memorization in learning and explain the disparate effects that privacy and model compression have on different subgroups.
Artificial Intelligence Enabled Material Behavior Prediction
Authors: Timothy Hanlon, Johan Reimann, Monica A. Soare, Anjali Singhal, James Grande, Marc Edgar, Kareem S. Aggour, Joseph Vinciquerra
Date: 2019-06-12 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Property Prediction

Artificial Intelligence and Machine Learning algorithms have considerable potential to influence the prediction of material properties. Additive materials have a unique property prediction challenge in the form of surface roughness effects on fatigue behavior of structural components. Traditional approaches using finite element methods to calculate stress risers associated with additively built surfaces have been challenging due to the computational resources required, often taking over a day to calculate a single sample prediction. To address this performance challenge, Deep Learning has been employed to enable low cycle fatigue life prediction in additive materials in a matter of seconds.
Differential Imaging Forensics
Authors: Aurélien Bourquard, Jeff Yan
Date: 2019-06-12 00:00:00

We introduce some new forensics based on differential imaging, where a novel category of visual evidence created via subtle interactions of light with a scene, such as dim reflections, can be computationally extracted and amplified from an image of interest through a comparative analysis with an additional reference baseline image acquired under similar conditions. This paradigm of differential imaging forensics (DIF) enables forensic examiners for the first time to retrieve the said visual evidence that is readily available in an image or video footage but would otherwise remain faint or even invisible to a human observer. We demonstrate the relevance and effectiveness of our approach through practical experiments. We also show that DIF provides a novel method for detecting forged images and video clips, including deep fakes.
GluonTS: Probabilistic Time Series Models in Python
Authors: Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang
Date: 2019-06-12 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.
LAEO-Net: revisiting people Looking At Each Other in videos
Authors: Manuel J. Marin-Jimenez, Vicky Kalogeiton, Pablo Medina-Suarez, Andrew Zisserman
Date: 2019-06-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Mutual Gaze

Capturing the `mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEONet to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net to social network analysis, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO.
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications
Authors: Finn Kuusisto, John Steill, Zhaobin Kuang, James Thomson, David Page, Ron Stewart
Date: 2019-06-12 00:00:00

We present a simple text mining method that is easy to implement, requires minimal data collection and preparation, and is easy to use for proposing ranked associations between a list of target terms and a key phrase. We call this method KinderMiner, and apply it to two biomedical applications. The first application is to identify relevant transcription factors for cell reprogramming, and the second is to identify potential drugs for investigation in drug repositioning. We compare the results from our algorithm to existing data and state-of-the-art algorithms, demonstrating compelling results for both application areas. While we apply the algorithm here for biomedical applications, we argue that the method is generalizable to any available corpus of sufficient size.