pwc-archive/papers-with-abstracts huggingface dataset:
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
Authors: Xuanqi Gao, Juan Zhai, Shiqing Ma, Chao Shen, Yufei Chen, Qian Wang
Date: 2022-04-06 00:00:00
Tasks: Fairness

With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfortunately, model fairness and accuracy in many cases are contradictory goals to optimize. To solve this issue, there has been a number of work trying to improve model fairness by using an adversarial game in model level. This approach introduces an adversary that evaluates the fairness of a model besides its prediction accuracy on the main task, and performs joint-optimization to achieve a balanced result. In this paper, we noticed that when performing backward propagation based training, such contradictory phenomenon has shown on individual neuron level. Based on this observation, we propose FairNeuron, a DNN model automatic repairing tool, to mitigate fairness concerns and balance the accuracy-fairness trade-off without introducing another model. It works on detecting neurons with contradictory optimization directions from accuracy and fairness training goals, and achieving a trade-off by selective dropout. Comparing with state-of-the-art methods, our approach is lightweight, making it scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron can effectively improve all models' fairness while maintaining a stable utility.
Simple and Effective Synthesis of Indoor 3D Scenes
Authors: Jing Yu Koh, Harsh Agrawal, Dhruv Batra, Richard Tucker, Austin Waters, Honglak Lee, Yinfei Yang, Jason Baldridge, Peter Anderson
Date: 2022-04-06 00:00:00
Tasks: Data Augmentation, Vision and Language Navigation

We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A vision-and-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the R2R benchmark. Our code will be made available to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks.
Beam Search: Faster and Monotonic
Authors: Sofia Lemons, Carlos Linares López, Robert C. Holte, Wheeler Ruml
Date: 2022-04-06 00:00:00
Tasks: Heuristic Search

Beam search is a popular satisficing approach to heuristic search problems that allows one to trade increased computation time for lower solution cost by increasing the beam width parameter. We make two contributions to the study of beam search. First, we show how to make beam search monotonic; that is, we provide a new variant that guarantees non-increasing solution cost as the beam width is increased. This makes setting the beam parameter much easier. Second, we show how using distance-to-go estimates can allow beam search to find better solutions more quickly in domains with non-uniform costs. Together, these results improve the practical effectiveness of beam search.
A survey on recently proposed activation functions for Deep Learning
Authors: Murilo Gustineli
Date: 2022-04-06 00:00:00
Tasks: Deep Learning, speech-recognition, Speech Recognition

Artificial neural networks (ANN), typically referred to as neural networks, are a class of Machine Learning algorithms and have achieved widespread success, having been inspired by the biological structure of the human brain. Neural networks are inherently powerful due to their ability to learn complex function approximations from data. This generalization ability has been able to impact multidisciplinary areas involving image recognition, speech recognition, natural language processing, and others. Activation functions are a crucial sub-component of neural networks. They define the output of a node in the network given a set of inputs. This survey discusses the main concepts of activation functions in neural networks, including; a brief introduction to deep neural networks, a summary of what are activation functions and how they are used in neural networks, their most common properties, the different types of activation functions, some of the challenges, limitations, and alternative solutions faced by activation functions, concluding with the final remarks.
Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask
Authors: Bilal Ghanem, Lauren Lutz Coleman, Julia Rivard Dexter, Spencer McIntosh von der Ohe, Alona Fyshe
Date: 2022-04-06 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: Question Generation, Question-Generation, Reading Comprehension

Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improve retention. Historically such questions were written by skilled teachers, but recently language models have been used to generate comprehension questions. However, many existing Question Generation (QG) systems focus on generating literal questions from the text, and have no way to control the type of the generated question. In this paper, we study QG for reading comprehension where inferential questions are critical and extractive techniques cannot be used. We propose a two-step model (HTA-WTA) that takes advantage of previous datasets, and can generate questions for a specific targeted comprehension skill. We propose a new reading comprehension dataset that contains questions annotated with story-based reading comprehension skills (SBRCS), allowing for a more complete reader assessment. Across several experiments, our results show that HTA-WTA outperforms multiple strong baselines on this new dataset. We show that the HTA-WTA model tests for strong SCRS by asking deep inferential questions.
A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents
Authors: Andrew Fuchs, Andrea Passarella, Marco Conti
Date: 2022-04-06 00:00:00

With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human. In the case of humans and artificial AI agents operating in the same environment, we note the significance of comprehension and response to the actions or capabilities of a human from an agent's perspective, as well as the possibility to delegate decisions either to humans or to agents, depending on who is deemed more suitable at a certain point in time. Such capabilities will ensure an improved responsiveness and utility of the entire human-AI system. To that end, we investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior, and associated performance with respect to a certain goal, is used to delegate control between humans and AI agents through the use of an intermediary entity. As we demonstrate, this allows overcoming potential shortcomings of either humans or agents in the pursuit of a goal.
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Authors: Tong Sang, Hongyao Tang, Yi Ma, Jianye Hao, Yan Zheng, Zhaopeng Meng, Boyan Li, Zhen Wang
Date: 2022-04-06 00:00:00
Tasks: Contrastive Learning, Decision Making, Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments prevent widespread application of DRL agents in real-world scenarios. Although advances have been made recently, most prior works assume sufficient online interaction on training environments, which can be costly in practical cases. To this end, we focus on an offline-training-online-adaptation setting, in which the agent first learns from offline experiences collected in environments with different dynamics and then performs online policy adaptation in environments with new dynamics. In this paper, we propose Policy Adaptation with Decoupled Representations (PAnDR) for fast policy adaptation. In offline training phase, the environment representation and policy representation are learned through contrastive learning and policy recovery, respectively. The representations are further refined by mutual information optimization to make them more decoupled and complete. With learned representations, a Policy-Dynamics Value Function (PDVF) [Raileanu et al., 2020] network is trained to approximate the values for different combinations of policies and environments from offline experiences. In online adaptation phase, with the environment context inferred from few experiences collected in new environments, the policy is optimized by gradient ascent with respect to the PDVF. Our experiments show that PAnDR outperforms existing algorithms in several representative policy adaptation problems.
ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound
Authors: Yan-Bo Lin, Jie Lei, Mohit Bansal, Gedas Bertasius
Date: 2022-04-06 00:00:00
Tasks: Retrieval, Text to Video Retrieval, Video Retrieval

We introduce an audiovisual method for long-range text-to-video retrieval. Unlike previous approaches designed for short video retrieval (e.g., 5-15 seconds in duration), our approach aims to retrieve minute-long videos that capture complex human actions. One challenge of standard video-only approaches is the large computational cost associated with processing hundreds of densely extracted frames from such long videos. To address this issue, we propose to replace parts of the video with compact audio cues that succinctly summarize dynamic audio events and are cheap to process. Our method, named ECLIPSE (Efficient CLIP with Sound Encoding), adapts the popular CLIP model to an audiovisual video setting, by adding a unified audiovisual transformer block that captures complementary cues from the video and audio streams. In addition to being 2.92x faster and 2.34x memory-efficient than long-range video-only approaches, our method also achieves better text-to-video retrieval accuracy on several diverse long-range video datasets such as ActivityNet, QVHighlights, YouCook2, DiDeMo and Charades.
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning
Authors: Eugene Valassakis, Georgios Papagiannis, Norman Di Palo, Edward Johns
Date: 2022-04-06 00:00:00
Tasks: Imitation Learning, Object, Semantic Segmentation

We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training. DOME does not require prior task or object knowledge, and can perform the task in novel object configurations and with distractors. At its core, DOME uses an image-conditioned object segmentation network followed by a learned visual servoing network, to move the robot's end-effector to the same relative pose to the object as during the demonstration, after which the task can be completed by replaying the demonstration's end-effector velocities. We show that DOME achieves near 100% success rate on 7 real-world everyday tasks, and we perform several studies to thoroughly understand each individual component of DOME. Videos and supplementary material are available at: https://www.robot-learning.uk/dome .
KNN-Diffusion: Image Generation via Large-Scale Retrieval
Authors: Shelly Sheynin, Oron Ashual, Adam Polyak, Uriel Singer, Oran Gafni, Eliya Nachmani, Yaniv Taigman
Date: 2022-04-06 00:00:00
Tasks: Image Generation, Retrieval, Text to Image Generation, Text-to-Image Generation

Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains where data is scarce or not labeled. In this work, we propose using large-scale retrieval methods, in particular, efficient k-Nearest-Neighbors (kNN), which offers novel capabilities: (1) training a substantially small and efficient text-to-image diffusion model without any text, (2) generating out-of-distribution images by simply swapping the retrieval database at inference time, and (3) performing text-driven local semantic manipulations while preserving object identity. To demonstrate the robustness of our method, we apply our kNN approach on two state-of-the-art diffusion backbones, and show results on several different datasets. As evaluated by human studies and automatic metrics, our method achieves state-of-the-art results compared to existing approaches that train text-to-image generation models using images only (without paired text data)
Data-Centric Green AI: An Exploratory Empirical Study
Authors: Roberto Verdecchia, Luís Cruz, June Sallou, Michelle Lin, James Wickenden, Estelle Hotellier
Date: 2022-04-06 00:00:00
Tasks: Open-Ended Question Answering

With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if data-centric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.
Adversarial Learning to Reason in an Arbitrary Logic
Authors: Stanisław J. Purgał, Cezary Kaliszyk
Date: 2022-04-06 00:00:00
Tasks: Automated Theorem Proving

Existing approaches to learning to prove theorems focus on particular logics and datasets. In this work, we propose Monte-Carlo simulations guided by reinforcement learning that can work in an arbitrarily specified logic, without any human knowledge or set of problems. Since the algorithm does not need any training dataset, it is able to learn to work with any logical foundation, even when there is no body of proofs or even conjectures available. We practically demonstrate the feasibility of the approach in multiple logical systems. The approach is stronger than training on randomly generated data but weaker than the approaches trained on tailored axiom and conjecture sets. It however allows us to apply machine learning to automated theorem proving for many logics, where no such attempts have been tried to date, such as intuitionistic logic or linear logic.
Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings
Authors: Gaurav Pandey, Danish Contractor, Sachindra Joshi
Date: 2022-04-06 00:00:00
Tasks: Retrieval

Embedding-based approaches for dialog response retrieval embed the context-response pairs as points in the embedding space. These approaches are scalable, but fail to account for the complex, many-to-many relationships that exist between context-response pairs. On the other end of the spectrum, there are approaches that feed the context-response pairs jointly through multiple layers of neural networks. These approaches can model the complex relationships between context-response pairs, but fail to scale when the set of responses is moderately large (>100). In this paper, we combine the best of both worlds by proposing a scalable model that can learn complex relationships between context-response pairs. Specifically, the model maps the contexts as well as responses to probability distributions over the embedding space. We train the models by optimizing the Kullback-Leibler divergence between the distributions induced by context-response pairs in the training data. We show that the resultant model achieves better performance as compared to other embedding-based approaches on publicly available conversation data.
Learning to Adapt Clinical Sequences with Residual Mixture of Experts
Authors: Jeong Min Lee, Milos Hauskrecht
Date: 2022-04-06 00:00:00
Tasks: Mixture-of-Experts

Clinical event sequences in Electronic Health Records (EHRs) record detailed information about the patient condition and patient care as they occur in time. Recent years have witnessed increased interest of machine learning community in developing machine learning models solving different types of problems defined upon information in EHRs. More recently, neural sequential models, such as RNN and LSTM, became popular and widely applied models for representing patient sequence data and for predicting future events or outcomes based on such data. However, a single neural sequential model may not properly represent complex dynamics of all patients and the differences in their behaviors. In this work, we aim to alleviate this limitation by refining a one-fits-all model using a Mixture-of-Experts (MoE) architecture. The architecture consists of multiple (expert) RNN models covering patient sub-populations and refining the predictions of the base model. That is, instead of training expert RNN models from scratch we define them on the residual signal that attempts to model the differences from the population-wide model. The heterogeneity of various patient sequences is modeled through multiple experts that consist of RNN. Particularly, instead of directly training MoE from scratch, we augment MoE based on the prediction signal from pretrained base GRU model. With this way, the mixture of experts can provide flexible adaptation to the (limited) predictive power of the single base RNN model. We experiment with the newly proposed model on real-world EHRs data and the multivariate clinical event prediction task. We implement RNN using Gated Recurrent Units (GRU). We show 4.1% gain on AUPRC statistics compared to a single RNN prediction.
SecureBERT: A Domain-Specific Language Model for Cybersecurity
Authors: Ehsan Aghaei, Xi Niu, Waseem Shadid, Ehab Al-Shaer
Date: 2022-04-06 00:00:00
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, model, Transfer Learning

Natural Language Processing (NLP) has recently gained wide attention in cybersecurity, particularly in Cyber Threat Intelligence (CTI) and cyber automation. Increased connection and automation have revolutionized the world's economic and cultural infrastructures, while they have introduced risks in terms of cyber attacks. CTI is information that helps cybersecurity analysts make intelligent security decisions, that is often delivered in the form of natural language text, which must be transformed to machine readable format through an automated procedure before it can be used for automated security measures. This paper proposes SecureBERT, a cybersecurity language model capable of capturing text connotations in cybersecurity text (e.g., CTI) and therefore successful in automation for many critical cybersecurity tasks that would otherwise rely on human expertise and time-consuming manual efforts. SecureBERT has been trained using a large corpus of cybersecurity text.To make SecureBERT effective not just in retaining general English understanding, but also when applied to text with cybersecurity implications, we developed a customized tokenizer as well as a method to alter pre-trained weights. The SecureBERT is evaluated using the standard Masked Language Model (MLM) test as well as two additional standard NLP tasks. Our evaluation studies show that SecureBERT\footnote{\url{https://github.com/ehsanaghaei/SecureBERT}} outperforms existing similar models, confirming its capability for solving crucial NLP tasks in cybersecurity.
PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model
Authors: Juncai Peng, Yi Liu, Shiyu Tang, Yuying Hao, Lutao Chu, Guowei Chen, Zewu Wu, Zeyu Chen, Zhiliang Yu, Yuning Du, Qingqing Dang, Baohua Lai, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma
Date: 2022-04-06 00:00:00
Tasks: Decoder, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of real-time methods is not satisfactory. In this work, we propose PP-LiteSeg, a novel lightweight model for the real-time semantic segmentation task. Specifically, we present a Flexible and Lightweight Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen feature representations, we propose a Unified Attention Fusion Module (UAFM), which takes advantage of spatial and channel attention to produce a weight and then fuses the input features with the weight. Moreover, a Simple Pyramid Pooling Module (SPPM) is proposed to aggregate global context with low computation cost. Extensive evaluations demonstrate that PP-LiteSeg achieves a superior trade-off between accuracy and speed compared to other methods. On the Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5% mIoU/102.6 FPS on NVIDIA GTX 1080Ti. Source code and models are available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.
Disentangling the Computational Complexity of Network Untangling
Authors: Vincent Froese, Pascal Kunz, Philipp Zschoche
Date: 2022-04-06 00:00:00

We study the network untangling problem introduced by Rozenshtein, Tatti, and Gionis [DMKD 2021], which is a variant of Vertex Cover on temporal graphs -- graphs whose edge set changes over discrete time steps. They introduce two problem variants. The goal is to select at most $k$ time intervals for each vertex such that all time-edges are covered and (depending on the problem variant) either the maximum interval length or the total sum of interval lengths is minimized. This problem has data mining applications in finding activity timelines that explain the interactions of entities in complex networks. Both variants of the problem are NP-hard. In this paper, we initiate a multivariate complexity analysis involving the following parameters: number of vertices, lifetime of the temporal graph, number of intervals per vertex, and the interval length bound. For both problem versions, we (almost) completely settle the parameterized complexity for all combinations of those four parameters, thereby delineating the border of fixed-parameter tractability.