pwc-archive/papers-with-abstracts huggingface dataset:
Annotated Guidelines and Building Reference Corpus for Myanmar-English Word Alignment
Authors: Nway Nway Han, Aye Thida
Date: 2019-09-25 00:00:00
Tasks: Contrastive Learning, Word Alignment

Reference corpus for word alignment is an important resource for developing and evaluating word alignment methods. For Myanmar-English language pairs, there is no reference corpus to evaluate the word alignment tasks. Therefore, we created the guidelines for Myanmar-English word alignment annotation between two languages over contrastive learning and built the Myanmar-English reference corpus consisting of verified alignments from Myanmar ALT of the Asian Language Treebank (ALT). This reference corpus contains confident labels sure (S) and possible (P) for word alignments which are used to test for the purpose of evaluation of the word alignments tasks. We discuss the most linking ambiguities to define consistent and systematic instructions to align manual words. We evaluated the results of annotators agreement using our reference corpus in terms of alignment error rate (AER) in word alignment tasks and discuss the words relationships in terms of BLEU scores.
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
Authors: Zehao Lin, Xinjing Huang, Feng Ji, Haiqing Chen, Ying Zhang
Date: 2019-09-25 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promising way. However, existing memory networks do not perform well when leveraging heterogeneous information from different sources. In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples. In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory. During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary. Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art data-driven task-oriented dialogue models in most domains.
Stochastic Conditional Generative Networks with Basis Decomposition
Authors: Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu
Date: 2019-09-25 00:00:00
Proceeding: ICLR 2020 1
Tasks: Conditional Image Generation, Diversity, Image Generation

While generative adversarial networks (GANs) have revolutionized machine learning, a number of open questions remain to fully understand them and exploit their power. One of these questions is how to efficiently achieve proper diversity and sampling of the multi-mode data space. To address this, we introduce BasisGAN, a stochastic conditional multi-mode image generator. By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of basis elements, we learn a plug-and-played basis generator to stochastically generate basis elements, with just a few hundred of parameters, to fully embed stochasticity into convolutional filters. By sampling basis elements instead of filters, we dramatically reduce the cost of modeling the parameter space with no sacrifice on either image diversity or fidelity. To illustrate this proposed plug-and-play framework, we construct variants of BasisGAN based on state-of-the-art conditional image generation networks, and train the networks by simply plugging in a basis generator, without additional auxiliary components, hyperparameters, or training objectives. The experimental success is complemented with theoretical results indicating how the perturbations introduced by the proposed sampling of basis elements can propagate to the appearance of generated images.
A Dictionary Approach to Domain-Invariant Learning in Deep Networks
Authors: Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu
Date: 2019-09-25 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Domain Adaptation

In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN). By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of dictionary atoms, we show for the first time, both empirically and theoretically, that domain shifts can be effectively handled by decomposing a convolutional layer into a domain-specific atom layer and a domain-shared coefficient layer, while both remain convolutional. An input channel will now first convolve spatially only with each respective domain-specific dictionary atom to "absorb" domain variations, and then output channels are linearly combined using common decomposition coefficients trained to promote shared semantics across domains. We use toy examples, rigorous analysis, and real-world examples with diverse datasets and architectures, to show the proposed plug-in framework's effectiveness in cross and joint domain performance and domain adaptation. With the proposed architecture, we need only a small set of dictionary atoms to model each additional domain, which brings a negligible amount of additional parameters, typically a few hundred.
Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
Authors: Irwandi Hipiny, Hamimah Ujir, Aazani Mujahid, Nurhartini Kamalia Yahya
Date: 2019-09-25 00:00:00
Tasks: Classification, General Classification, Position, Retrieval

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, we collected images of sea turtle carapace, each belonging to one of sixteen Chelonia mydas juveniles. We then learned co-variant and robust image descriptors from these images, enabling indexing and retrieval. In this work, we presented several classification results of sea turtle carapaces using the learned image descriptors. We found that a template-based descriptor, i.e., Histogram of Oriented Gradients (HOG) performed exceedingly better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must due to the minimal gradient and color information inside the carapace images. Using HOG, we obtained an average classification accuracy of 65%.
Switched linear projections for neural network interpretability
Authors: Lech Szymanski, Brendan McCane, Craig Atkinson
Date: 2019-09-25 00:00:00

We introduce switched linear projections for expressing the activity of a neuron in a deep neural network in terms of a single linear projection in the input space. The method works by isolating the active subnetwork, a series of linear transformations, that determine the entire computation of the network for a given input instance. With these projections we can decompose activity in any hidden layer into patterns detected in a given input instance. We also propose that in ReLU networks it is instructive and meaningful to examine patterns that deactivate the neurons in a hidden layer, something that is implicitly ignored by the existing interpretability methods tracking solely the active aspect of the network's computation.
Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
Authors: Taiji Suzuki, Hiroshi Abe, Tomoaki Nishimura
Date: 2019-09-25 00:00:00
Proceeding: ICLR 2020 1
Tasks: Learning Theory

One of the biggest issues in deep learning theory is the generalization ability of networks with huge model size. The classical learning theory suggests that overparameterized models cause overfitting. However, practically used large deep models avoid overfitting, which is not well explained by the classical approaches. To resolve this issue, several attempts have been made. Among them, the compression based bound is one of the promising approaches. However, the compression based bound can be applied only to a compressed network, and it is not applicable to the non-compressed original network. In this paper, we give a unified frame-work that can convert compression based bounds to those for non-compressed original networks. The bound gives even better rate than the one for the compressed network by improving the bias term. By establishing the unified frame-work, we can obtain a data dependent generalization error bound which gives a tighter evaluation than the data independent ones.
TalkDown: A Corpus for Condescension Detection in Context
Authors: Zijian Wang, Christopher Potts
Date: 2019-09-25 00:00:00
Proceeding: IJCNLP 2019 11

Condescending language use is caustic; it can bring dialogues to an end and bifurcate communities. Thus, systems for condescension detection could have a large positive impact. A challenge here is that condescension is often impossible to detect from isolated utterances, as it depends on the discourse and social context. To address this, we present TalkDown, a new labeled dataset of condescending linguistic acts in context. We show that extending a language-only model with representations of the discourse improves performance, and we motivate techniques for dealing with the low rates of condescension overall. We also use our model to estimate condescension rates in various online communities and relate these differences to differing community norms.
Automated identification of neural cells in the multi-photon images using deep-neural networks
Authors: Si-Baek Seong, Hae-Jeong Park
Date: 2019-09-25 00:00:00
Tasks: Classification, General Classification, Transfer Learning

The advancement of the neuroscientific imaging techniques has produced an unprecedented size of neural cell imaging data, which calls for automated processing. In particular, identification of cells from two photon images demands segmentation of neural cells out of various materials and classification of the segmented cells according to their cell types. To automatically segment neural cells, we used U-Net model, followed by classification of excitatory and inhibitory neurons and glia cells using a transfer learning technique. For transfer learning, we tested three public models of resnet18, resnet50 and inceptionv3, after replacing the fully connected layer with that for three classes. The best classification performance was found for the model with inceptionv3. The proposed application of deep learning technique is expected to provide a critical way to cell identification in the era of big neuroscience data.
Rescan: Inductive Instance Segmentation for Indoor RGBD Scans
Authors: Maciej Halber, Yifei Shi, Kai Xu, Thomas Funkhouser
Date: 2019-09-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e.g., as part of regular daily use). We propose an algorithm that analyzes these "rescans" to infer a temporal model of a scene with semantic instance information. Our algorithm operates inductively by using the temporal model resulting from past observations to infer an instance segmentation of a new scan, which is then used to update the temporal model. The model contains object instance associations across time and thus can be used to track individual objects, even though there are only sparse observations. During experiments with a new benchmark for the new task, our algorithm outperforms alternate approaches based on state-of-the-art networks for semantic instance segmentation.
PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space
Authors: Omer Anjum, Hongyu Gong, Suma Bhat, Wen-mei Hwu, JinJun Xiong
Date: 2019-09-25 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Topic Models

Finding the right reviewers to assess the quality of conference submissions is a time consuming process for conference organizers. Given the importance of this step, various automated reviewer-paper matching solutions have been proposed to alleviate the burden. Prior approaches, including bag-of-words models and probabilistic topic models have been inadequate to deal with the vocabulary mismatch and partial topic overlap between a paper submission and the reviewer's expertise. Our approach, the common topic model, jointly models the topics common to the submission and the reviewer's profile while relying on abstract topic vectors. Experiments and insightful evaluations on two datasets demonstrate that the proposed method achieves consistent improvements compared to available state-of-the-art implementations of paper-reviewer matching.
Neighborhood-Enhanced and Time-Aware Model for Session-based Recommendation
Authors: Yang Lv, Liangsheng Zhuang, Pengyu Luo
Date: 2019-09-25 00:00:00
Tasks: Recommendation Systems, Session-Based Recommendations

Session based recommendation has become one of the research hotpots in the field of recommendation systems due to its highly practical value.Previous deep learning methods mostly focus on the sequential characteristics within the current session,and neglect the context similarity and temporal similarity between sessions which contain abundant collaborative information.In this paper,we propose a novel neural networks framework,namely Neighborhood Enhanced and Time Aware Recommendation Machine(NETA) for session based recommendation. Firstly,we introduce an efficient neighborhood retrieve mechanism to find out similar sessions which includes collaborative information.Then we design a guided attention with time-aware mechanism to extract collaborative representation from neighborhood sessions.Especially,temporal recency between sessions is considered separately.Finally, we design a simple co-attention mechanism to determine the importance of complementary collaborative representation when predicting the next item.Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our proposed model.
Online Semi-Supervised Concept Drift Detection with Density Estimation
Authors: Chang How Tan, Vincent CS Lee, Mahsa Salehi
Date: 2019-09-25 00:00:00
Tasks: Density Estimation, Drift Detection

Concept drift is formally defined as the change in joint distribution of a set of input variables X and a target variable y. The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distribution of X without affecting the posterior probabilities. Many approaches on concept drift detection either assume full availability of data labels, y or handle only the virtual drift. In a streaming environment, the assumption of full availability of data labels, y is questioned. On the other hand, approaches that deal with virtual drift failed to address real drift. Rather than improving the state-of-the-art methods, this paper presents a semi-supervised framework to deal with the challenges above. The objective of the proposed framework is to learn from streaming environment with limited data labels, y and detect real drift concurrently. This paper proposes a novel concept drift detection method utilizing the densities of posterior probabilities in partially labeled streaming environments. Experimental results on both synthetic and realworld datasets show that our proposed semi-supervised framework enables the detection of concept drift in such environment while achieving comparable prediction performance to the state-of-the-art methods.
When to Intervene: Detecting Abnormal Mood using Everyday Smartphone Conversations
Authors: John Gideon, Katie Matton, Steve Anderau, Melvin G McInnis, Emily Mower Provost
Date: 2019-09-25 00:00:00
Tasks: Anomaly Detection

Bipolar disorder (BPD) is a chronic mental illness characterized by extreme mood and energy changes from mania to depression. These changes drive behaviors that often lead to devastating personal or social consequences. BPD is managed clinically with regular interactions with care providers, who assess mood, energy levels, and the form and content of speech. Recent work has proposed smartphones for monitoring mood using speech. However, these works do not predict when to intervene. Predicting when to intervene is challenging because there is not a single measure that is relevant for every person: different individuals may have different levels of symptom severity considered typical. Additionally, this typical mood, or baseline, may change over time, making a single symptom threshold insufficient. This work presents an innovative approach that expands clinical mood monitoring to predict when interventions are necessary using an anomaly detection framework, which we call Temporal Normalization. We first validate the model using a dataset annotated for clinical interventions and then incorporate this method in a deep learning framework to predict mood anomalies from natural, unstructured, telephone speech data. The combination of these approaches provides a framework to enable real-world speech-focused mood monitoring.
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis
Authors: Franco M. Luque
Date: 2019-09-25 00:00:00
Tasks: Data Augmentation, Sentiment Analysis, Translation, Word Embeddings

In this article we describe our participation in TASS 2019, a shared task aimed at the detection of sentiment polarity of Spanish tweets. We combined different representations such as bag-of-words, bag-of-characters, and tweet embeddings. In particular, we trained robust subword-aware word embeddings and computed tweet representations using a weighted-averaging strategy. We also used two data augmentation techniques to deal with data scarcity: two-way translation augmentation, and instance crossover augmentation, a novel technique that generates new instances by combining halves of tweets. In experiments, we trained linear classifiers and ensemble models, obtaining highly competitive results despite the simplicity of our approaches.
Learning Propagation for Arbitrarily-structured Data
Authors: Sifei Liu, Xueting Li, Varun Jampani, Shalini De Mello, Jan Kautz
Date: 2019-09-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Point Cloud Segmentation, Segmentation, Semantic Segmentation, Superpixels

Processing an input signal that contains arbitrary structures, e.g., superpixels and point clouds, remains a big challenge in computer vision. Linear diffusion, an effective model for image processing, has been recently integrated with deep learning algorithms. In this paper, we propose to learn pairwise relations among data points in a global fashion to improve semantic segmentation with arbitrarily-structured data, through spatial generalized propagation networks (SGPN). The network propagates information on a group of graphs, which represent the arbitrarily-structured data, through a learned, linear diffusion process. The module is flexible to be embedded and jointly trained with many types of networks, e.g., CNNs. We experiment with semantic segmentation networks, where we use our propagation module to jointly train on different data -- images, superpixels and point clouds. We show that SGPN consistently improves the performance of both pixel and point cloud segmentation, compared to networks that do not contain this module. Our method suggests an effective way to model the global pairwise relations for arbitrarily-structured data.
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
Authors: Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust, Guido C. H. E. de Croon, Vijay Janapa Reddi
Date: 2019-09-25 00:00:00
Tasks: Autonomous Navigation, Deep Reinforcement Learning, Efficient Exploration, Quantization, Reinforcement Learning

We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter. Our deep-RL algorithm finds a high-performance solution to a challenging problem, even in presence of high noise levels and generalizes across real and simulation environments with different obstacle configurations. We verify our approach with simulation and in-field testing on a Bitcraze CrazyFlie using only the cheap and ubiquitous Cortex-M4 microcontroller unit. The results show that by end-to-end application-specific system design, our contribution consumes almost three times less additional power, as compared to competing learning-based navigation approach onboard a nano quadcopter. Thanks to our observation space, which we carefully design within the resource constraints, our solution achieves a 94% success rate in cluttered and randomized test environments, as compared to the previously achieved 80%. We also compare our strategy to a simple finite state machine (FSM), geared towards efficient exploration, and demonstrate that our policy is more robust and resilient at obstacle avoidance as well as up to 70% more efficient in source seeking. To this end, we contribute a cheap and lightweight end-to-end tiny robot learning (tinyRL) solution, running onboard a nano quadcopter, that proves to be robust and efficient in a challenging task using limited sensory input.
Teacher-Student Learning Paradigm for Tri-training: An Efficient Method for Unlabeled Data Exploitation
Authors: Yash Bhalgat, Zhe Liu, Pritam Gundecha, Jalal Mahmud, Amita Misra
Date: 2019-09-25 00:00:00
Tasks: Sentiment Analysis

Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data. Traditional tri-training algorithm and tri-training with disagreement have shown promise in tasks where labeled data is limited. In this work, we introduce a new paradigm for tri-training, mimicking the real world teacher-student learning process. We show that the adaptive teacher-student thresholds used in the proposed method provide more control over the learning process with higher label quality. We perform evaluation on SemEval sentiment analysis task and provide comprehensive comparisons over experimental settings containing varied labeled versus unlabeled data rates. Experimental results show that our method outperforms other strong semi-supervised baselines, while requiring less number of labeled training samples.