pwc-archive/papers-with-abstracts huggingface dataset:
Why Do Better Loss Functions Lead to Less Transferable Features?
Authors: Simon Kornblith, Ting Chen, Honglak Lee, Mohammad Norouzi
Date: 2020-10-30 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: General Classification, image-classification, Image Classification

Previous work has proposed many new loss functions and regularizers that improve test accuracy on image classification tasks. However, it is not clear whether these loss functions learn better representations for downstream tasks. This paper studies how the choice of training objective affects the transferability of the hidden representations of convolutional neural networks trained on ImageNet. We show that many objectives lead to statistically significant improvements in ImageNet accuracy over vanilla softmax cross-entropy, but the resulting fixed feature extractors transfer substantially worse to downstream tasks, and the choice of loss has little effect when networks are fully fine-tuned on the new tasks. Using centered kernel alignment to measure similarity between hidden representations of networks, we find that differences among loss functions are apparent only in the last few layers of the network. We delve deeper into representations of the penultimate layer, finding that different objectives and hyperparameter combinations lead to dramatically different levels of class separation. Representations with higher class separation obtain higher accuracy on the original task, but their features are less useful for downstream tasks. Our results suggest there exists a trade-off between learning invariant features for the original task and features relevant for transfer tasks.
Goal directed molecule generation using Monte Carlo Tree Search
Authors: Anand A. Rajasekar, Karthik Raman, Balaraman Ravindran
Date: 2020-10-30 00:00:00
Tasks: Navigate

One challenging and essential task in biochemistry is the generation of novel molecules with desired properties. Novel molecule generation remains a challenge since the molecule space is difficult to navigate through, and the generated molecules should obey the rules of chemical valency. Through this work, we propose a novel method, which we call unitMCTS, to perform molecule generation by making a unit change to the molecule at every step using Monte Carlo Tree Search. We show that this method outperforms the recently published techniques on benchmark molecular optimization tasks such as QED and penalized logP. We also demonstrate the usefulness of this method in improving molecule properties while being similar to the starting molecule. Given that there is no learning involved, our method finds desired molecules within a shorter amount of time.
Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss
Authors: Panagiotis Paraskevas Filntisis, Niki Efthymiou, Gerasimos Potamianos, Petros Maragos
Date: 2020-10-30 00:00:00
Tasks: Emotion Recognition, Word Embeddings

We present our winning submission to the First International Workshop on Bodily Expressed Emotion Understanding (BEEU) challenge. Based on recent literature on the effect of context/environment on emotion, as well as visual representations with semantic meaning using word embeddings, we extend the framework of Temporal Segment Network to accommodate these. Our method is verified on the validation set of the Body Language Dataset (BoLD) and achieves 0.26235 Emotion Recognition Score on the test set, surpassing the previous best result of 0.2530.
Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides
Authors: Mengdan Zhu, Bing Ren, Ryland Richards, Matthew Suriawinata, Naofumi Tomita, Saeed Hassanpour
Date: 2020-10-30 00:00:00
Tasks: Classification, Diagnostic, General Classification, Management, Prognosis

Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 69 randomly selected surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98, 0.98 and 0.99, respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion.
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition
Authors: Wei Zhou, Simon Berger, Ralf Schlüter, Hermann Ney
Date: 2020-10-30 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeling. Different alignment label topologies are compared and word-end-based phoneme label augmentation is proposed to improve performance. Utilizing the local dependency of phonemes, we adopt a simplified neural network structure and a straightforward integration with the external word-level language model to preserve the consistency of seq-to-seq modeling. We also present a simple, stable and efficient training procedure using frame-wise cross-entropy loss. A phonetic context size of one is shown to be sufficient for the best performance. A simplified scheduled sampling approach is applied for further improvement and different decoding approaches are briefly compared. The overall performance of our best model is comparable to state-of-the-art (SOTA) results for the TED-LIUM Release 2 and Switchboard corpora.
Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents
Authors: Gregory Yauney, Jack Hessel, David Mimno
Date: 2020-10-30 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Clustering, object-detection, Object Detection, Sentence

Images can give us insights into the contextual meanings of words, but current image-text grounding approaches require detailed annotations. Such granular annotation is rare, expensive, and unavailable in most domain-specific contexts. In contrast, unlabeled multi-image, multi-sentence documents are abundant. Can lexical grounding be learned from such documents, even though they have significant lexical and visual overlap? Working with a case study dataset of real estate listings, we demonstrate the challenge of distinguishing highly correlated grounded terms, such as "kitchen" and "bedroom", and introduce metrics to assess this document similarity. We present a simple unsupervised clustering-based method that increases precision and recall beyond object detection and image tagging baselines when evaluated on labeled subsets of the dataset. The proposed method is particularly effective for local contextual meanings of a word, for example associating "granite" with countertops in the real estate dataset and with rocky landscapes in a Wikipedia dataset.
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data
Authors: Romain Egele, Prasanna Balaprakash, Venkatram Vishwanath, Isabelle Guyon, Zhengying Liu
Date: 2020-10-30 00:00:00
Tasks: AutoML, Bayesian Optimization, Neural Architecture Search

Developing high-performing predictive models for large tabular data sets is a challenging task. The state-of-the-art methods are based on expert-developed model ensembles from different supervised learning methods. Recently, automated machine learning (AutoML) is emerging as a promising approach to automate predictive model development. Neural architecture search (NAS) is an AutoML approach that generates and evaluates multiple neural network architectures concurrently and improves the accuracy of the generated models iteratively. A key issue in NAS, particularly for large data sets, is the large computation time required to evaluate each generated architecture. While data-parallel training is a promising approach that can address this issue, its use within NAS is difficult. For different data sets, the data-parallel training settings such as the number of parallel processes, learning rate, and batch size need to be adapted to achieve high accuracy and reduction in training time. To that end, we have developed AgEBO-Tabular, an approach to combine aging evolution (AgE), a parallel NAS method that searches over neural architecture space, and an asynchronous Bayesian optimization method for tuning the hyperparameters of the data-parallel training simultaneously. We demonstrate the efficacy of the proposed method to generate high-performing neural network models for large tabular benchmark data sets. Furthermore, we demonstrate that the automatically discovered neural network models using our method outperform the state-of-the-art AutoML ensemble models in inference speed by two orders of magnitude while reaching similar accuracy values.
A Cross-lingual Natural Language Processing Framework for Infodemic Management
Authors: Ridam Pal, Rohan Pandey, Vaibhav Gautam, Kanav Bhagat, Tavpritesh Sethi
Date: 2020-10-30 00:00:00
Tasks: Articles, Management, Misinformation, Word Embeddings

The COVID-19 pandemic has put immense pressure on health systems which are further strained due to the misinformation surrounding it. Under such a situation, providing the right information at the right time is crucial. There is a growing demand for the management of information spread using Artificial Intelligence. Hence, we have exploited the potential of Natural Language Processing for identifying relevant information that needs to be disseminated amongst the masses. In this work, we present a novel Cross-lingual Natural Language Processing framework to provide relevant information by matching daily news with trusted guidelines from the World Health Organization. The proposed pipeline deploys various techniques of NLP such as summarizers, word embeddings, and similarity metrics to provide users with news articles along with a corresponding healthcare guideline. A total of 36 models were evaluated and a combination of LexRank based summarizer on Word2Vec embedding with Word Mover distance metric outperformed all other models. This novel open-source approach can be used as a template for proactive dissemination of relevant healthcare information in the midst of misinformation spread associated with epidemics.
Marginalised Gaussian Processes with Nested Sampling
Authors: Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Date: 2020-10-30 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Gaussian Processes

Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective. This classical approach known as Type-II maximum likelihood (ML-II) yields point estimates of the hyperparameters, and continues to be the default method for training GPs. However, this approach risks underestimating predictive uncertainty and is prone to overfitting especially when there are many hyperparameters. Furthermore, gradient based optimisation makes ML-II point estimates highly susceptible to the presence of local minima. This work presents an alternative learning procedure where the hyperparameters of the kernel function are marginalised using Nested Sampling (NS), a technique that is well suited to sample from complex, multi-modal distributions. We focus on regression tasks with the spectral mixture (SM) class of kernels and find that a principled approach to quantifying model uncertainty leads to substantial gains in predictive performance across a range of synthetic and benchmark data sets. In this context, nested sampling is also found to offer a speed advantage over Hamiltonian Monte Carlo (HMC), widely considered to be the gold-standard in MCMC based inference.
Leveraging Extracted Model Adversaries for Improved Black Box Attacks
Authors: Naveen Jafer Nizar, Ari Kobren
Date: 2020-10-30 00:00:00
Proceeding: EMNLP (BlackboxNLP) 2020 11
Tasks: Model extraction, Question Answering, Reading Comprehension

We present a method for adversarial input generation against black box models for reading comprehension based question answering. Our approach is composed of two steps. First, we approximate a victim black box model via model extraction (Krishna et al., 2020). Second, we use our own white box method to generate input perturbations that cause the approximate model to fail. These perturbed inputs are used against the victim. In experiments we find that our method improves on the efficacy of the AddAny---a white box attack---performed on the approximate model by 25% F1, and the AddSent attack---a black box attack---by 11% F1 (Jia and Liang, 2017).
Calibration-Aided Edge Inference Offloading via Adaptive Model Partitioning of Deep Neural Networks
Authors: Roberto G. Pacheco, Rodrigo S. Couto, Osvaldo Simeone
Date: 2020-10-30 00:00:00

Mobile devices can offload deep neural network (DNN)-based inference to the cloud, overcoming local hardware and energy limitations. However, offloading adds communication delay, thus increasing the overall inference time, and hence it should be used only when needed. An approach to address this problem consists of the use of adaptive model partitioning based on early-exit DNNs. Accordingly, the inference starts at the mobile device, and an intermediate layer estimates the accuracy: If the estimated accuracy is sufficient, the device takes the inference decision; Otherwise, the remaining layers of the DNN run at the cloud. Thus, the device offloads the inference to the cloud only if it cannot classify a sample with high confidence. This offloading requires a correct accuracy prediction at the device. Nevertheless, DNNs are typically miscalibrated, providing overconfident decisions. This work shows that the employment of a miscalibrated early-exit DNN for offloading via model partitioning can significantly decrease inference accuracy. In contrast, we argue that implementing a calibration algorithm prior to deployment can solve this problem, allowing for more reliable offloading decisions.
All of the Fairness for Edge Prediction with Optimal Transport
Authors: Charlotte Laclau, Ievgen Redko, Manvi Choudhary, Christine Largeron
Date: 2020-10-30 00:00:00
Tasks: All, Attribute, Decision Making, Fairness, Prediction

Machine learning and data mining algorithms have been increasingly used recently to support decision-making systems in many areas of high societal importance such as healthcare, education, or security. While being very efficient in their predictive abilities, the deployed algorithms sometimes tend to learn an inductive model with a discriminative bias due to the presence of this latter in the learning sample. This problem gave rise to a new field of algorithmic fairness where the goal is to correct the discriminative bias introduced by a certain attribute in order to decorrelate it from the model's output. In this paper, we study the problem of fairness for the task of edge prediction in graphs, a largely underinvestigated scenario compared to a more popular setting of fair classification. To this end, we formulate the problem of fair edge prediction, analyze it theoretically, and propose an embedding-agnostic repairing procedure for the adjacency matrix of an arbitrary graph with a trade-off between the group and individual fairness. We experimentally show the versatility of our approach and its capacity to provide explicit control over different notions of fairness and prediction accuracy.
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach
Authors: Ahmadreza Mosallanezhad, Kai Shu, Huan Liu
Date: 2020-10-30 00:00:00
Tasks: Deep Reinforcement Learning, Language Modeling, Language Modelling, News Generation, reinforcement-learning, Reinforcement Learning (RL)

Nowadays, there exist powerful language models such as OpenAI's GPT-2 that can generate readable text and can be fine-tuned to generate text for a specific domain. Considering GPT-2, it cannot directly generate synthetic news with respect to a given topic and the output of the language model cannot be explicitly controlled. In this paper, we study the novel problem of topic-preserving synthetic news generation. We propose a novel deep reinforcement learning-based method to control the output of GPT-2 with respect to a given news topic. When generating text using GPT-2, by default, the most probable word is selected from the vocabulary. Instead of selecting the best word each time from GPT-2's output, an RL agent tries to select words that optimize the matching of a given topic. In addition, using a fake news detector as an adversary, we investigate generating realistic news using our proposed method. In this paper, we consider realistic news as news that cannot be easily detected by a fake news classifier. Experimental results demonstrate the effectiveness of the proposed framework on generating topic-preserving news content than state-of-the-art baselines.
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
Authors: Tzvika Shapira, David Berend, Ishai Rosenberg, Yang Liu, Asaf Shabtai, Yuval Elovici
Date: 2020-10-30 00:00:00
Tasks: Malware Detection

The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training set, there is a need to continuously collect benign and malicious files from a wide range of sources, providing an exploitable target to attackers. In this study, we show how an attacker can launch a sophisticated and efficient poisoning attack targeting the dataset used to train a malware classifier. The attacker's ultimate goal is to ensure that the model induced by the poisoned dataset will be unable to detect the attacker's malware yet capable of detecting other malware. As opposed to other poisoning attacks in the malware detection domain, our attack does not focus on malware families but rather on specific malware instances that contain an implanted trigger, reducing the detection rate from 99.23% to 0% depending on the amount of poisoning. We evaluate our attack on the EMBER dataset with a state-of-the-art classifier and malware samples from VirusTotal for end-to-end validation of our work. We propose a comprehensive detection approach that could serve as a future sophisticated defense against this newly discovered severe threat.
DeepWay: a Deep Learning Waypoint Estimator for Global Path Generation
Authors: Vittorio Mazzia, Francesco Salvetti, Diego Aghi, Marcello Chiaberge
Date: 2020-10-30 00:00:00
Tasks: Deep Learning

Agriculture 3.0 and 4.0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield. Row-based crops are the perfect settings to test and deploy smart machines capable of monitoring and manage the harvest. In this context, global path generation is essential either for ground or aerial vehicles, and it is the starting point for every type of mission plan. Nevertheless, little attention has been currently given to this problem by the research community and global path generation automation is still far to be solved. In order to generate a viable path for an autonomous machine, the presented research proposes a feature learning fully convolutional model capable of estimating waypoints given an occupancy grid map. In particular, we apply the proposed data-driven methodology to the specific case of row-based crops with the general objective to generate a global path able to cover the extension of the crop completely. Extensive experimentation with a custom made synthetic dataset and real satellite-derived images of different scenarios have proved the effectiveness of our methodology and demonstrated the feasibility of an end-to-end and completely autonomous global path planner.
A Critical Assessment of State-of-the-Art in Entity Alignment
Authors: Max Berrendorf, Ludwig Wacker, Evgeniy Faerman
Date: 2020-10-30 00:00:00
Tasks: Benchmarking, Entity Alignment, Hyperparameter Optimization, Knowledge Graphs

In this work, we perform an extensive investigation of two state-of-the-art (SotA) methods for the task of Entity Alignment in Knowledge Graphs. Therefore, we first carefully examine the benchmarking process and identify several shortcomings, which make the results reported in the original works not always comparable. Furthermore, we suspect that it is a common practice in the community to make the hyperparameter optimization directly on a test set, reducing the informative value of reported performance. Thus, we select a representative sample of benchmarking datasets and describe their properties. We also examine different initializations for entity representations since they are a decisive factor for model performance. Furthermore, we use a shared train/validation/test split for a fair evaluation setting in which we evaluate all methods on all datasets. In our evaluation, we make several interesting findings. While we observe that most of the time SotA approaches perform better than baselines, they have difficulties when the dataset contains noise, which is the case in most real-life applications. Moreover, we find out in our ablation study that often different features of SotA methods are crucial for good performance than previously assumed. The code is available at https://github.com/mberr/ea-sota-comparison.
Embedding Meta-Textual Information for Improved Learning to Rank
Authors: Toshitaka Kuwa, Shigehiko Schamoni, Stefan Riezler
Date: 2020-10-30 00:00:00
Proceeding: COLING 2020 8
Tasks: Articles, Information Retrieval, Learning-To-Rank, Representation Learning, Retrieval

Neural approaches to learning term embeddings have led to improved computation of similarity and ranking in information retrieval (IR). So far neural representation learning has not been extended to meta-textual information that is readily available for many IR tasks, for example, patent classes in prior-art retrieval, topical information in Wikipedia articles, or product categories in e-commerce data. We present a framework that learns embeddings for meta-textual categories, and optimizes a pairwise ranking objective for improved matching based on combined embeddings of textual and meta-textual information. We show considerable gains in an experimental evaluation on cross-lingual retrieval in the Wikipedia domain for three language pairs, and in the Patent domain for one language pair. Our results emphasize that the mode of combining different types of information is crucial for model improvement.