pwc-archive/papers-with-abstracts huggingface dataset:
SocialInteractionGAN: Multi-person Interaction Sequence Generation
Authors: Louis Airale, Dominique Vaufreydaz, Xavier Alameda-Pineda
Date: 2021-03-10 00:00:00
Tasks: Decoder, multimodal interaction

Prediction of human actions in social interactions has important applications in the design of social robots or artificial avatars. In this paper, we focus on a unimodal representation of interactions and propose to tackle interaction generation in a data-driven fashion. In particular, we model human interaction generation as a discrete multi-sequence generation problem and present SocialInteractionGAN, a novel adversarial architecture for conditional interaction generation. Our model builds on a recurrent encoder-decoder generator network and a dual-stream discriminator, that jointly evaluates the realism of interactions and individual action sequences and operates at different time scales. Crucially, contextual information on interacting participants is shared among agents and reinjected in both the generation and the discriminator evaluation processes. Experiments show that albeit dealing with low dimensional data, SocialInteractionGAN succeeds in producing high realism action sequences of interacting people, comparing favorably to a diversity of recurrent and convolutional discriminator baselines, and we argue that this work will constitute a first stone towards higher dimensional and multimodal interaction generation. Evaluations are conducted using classical GAN metrics, that we specifically adapt for discrete sequential data. Our model is shown to properly learn the dynamics of interaction sequences, while exploiting the full range of available actions.
A Gradient Estimator for Time-Varying Electrical Networks with Non-Linear Dissipation
Authors: Jack Kendall
Date: 2021-03-09 00:00:00

We propose a method for extending the technique of equilibrium propagation for estimating gradients in fixed-point neural networks to the more general setting of directed, time-varying neural networks by modeling them as electrical circuits. We use electrical circuit theory to construct a Lagrangian capable of describing deep, directed neural networks modeled using nonlinear capacitors and inductors, linear resistors and sources, and a special class of nonlinear dissipative elements called fractional memristors. We then derive an estimator for the gradient of the physical parameters of the network, such as synapse conductances, with respect to an arbitrary loss function. This estimator is entirely local, in that it only depends on information locally available to each synapse. We conclude by suggesting methods for extending these results to networks of biologically plausible neurons, e.g. Hodgkin-Huxley neurons.
Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification
Authors: Amey Hengle, Atharva Kshirsagar, Shaily Desai, Manisha Marathe
Date: 2021-03-09 00:00:00
Proceeding: EACL (WANLP) 2021 4
Tasks: Language Modeling, Language Modelling, Sarcasm Detection, Sentence

Since their inception, transformer-based language models have led to impressive performance gains across multiple natural language processing tasks. For Arabic, the current state-of-the-art results on most datasets are achieved by the AraBERT language model. Notwithstanding these recent advancements, sarcasm and sentiment detection persist to be challenging tasks in Arabic, given the language's rich morphology, linguistic disparity and dialectal variations. This paper proffers team SPPU-AASM's submission for the WANLP ArSarcasm shared-task 2021, which centers around the sarcasm and sentiment polarity detection of Arabic tweets. The study proposes a hybrid model, combining sentence representations from AraBERT with static word vectors trained on Arabic social media corpora. The proposed system achieves a F1-sarcastic score of 0.62 and a F-PN score of 0.715 for the sarcasm and sentiment detection tasks, respectively. Simulation results show that the proposed system outperforms multiple existing approaches for both the tasks, suggesting that the amalgamation of context-free and context-dependent text representations can help capture complementary facets of word meaning in Arabic. The system ranked second and tenth in the respective sub-tasks of sarcasm detection and sentiment identification.
Continual Developmental Neurosimulation Using Embodied Computational Agents
Authors: Bradly Alicea, Rishabh Chakrabarty, Stefan Dvoretskii, Akshara Gopi, Avery Lim, Jesse Parent
Date: 2021-03-07 00:00:00
Tasks: Active Learning, Continual Learning, Developmental Learning

There is much to learn through synthesis of Developmental Biology, Cognitive Science and Computational Modeling. Our path forward involves a design for developmentally-inspired learning agents based on Braitenberg Vehicles. Continual developmental neurosimulation allows us to consider the role of developmental trajectories in bridging the related phenomena of nervous system morphogenesis, developmental learning, and plasticity. Being closely tied to continual learning, our approach is tightly integrated with developmental embodiment, and can be implemented using a type of agent called developmental Braitenberg Vehicles (dBVs). dBVs begin their lives as a set of undefined structures that transform into agent-based systems including a body, sensors, effectors, and nervous system. This phenotype is characterized in terms of developmental timing: with distinct morphogenetic, critical, and acquisition (developmental learning) periods. We further propose that network morphogenesis can be accomplished using a genetic algorithmic approach, while developmental learning can be implemented using a number of computational methodologies. This approach provides a framework for adaptive agent behavior that might result from a developmental approach: namely by exploiting critical periods or growth and acquisition, an explicitly embodied network architecture, and a distinction between the assembly of neuronal networks and active learning on these networks. In conclusion, we will consider agent learning and development at different timescales, from very short (<100ms) intervals to long-term evolution. The development, evolution, and learning in an embodied agent-based approach is key to an integrative view of biologically-inspired intelligence.
A New K means Grey Wolf Algorithm for Engineering Problems
Authors: Hardi M. Mohammed, Zrar Kh. Abdul, Tarik A. Rashid, Abeer Alsadoon, Nebojsa Bacanin
Date: 2021-02-27 00:00:00
Tasks: Clustering, Metaheuristic Optimization

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. Originality/value: Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA, and GWO so KMGWO achieved the first rank in terms of performance. Also, the KMGWO is used to solve a classical engineering problem and it is superior
The whole brain architecture approach: Accelerating the development of artificial general intelligence by referring to the brain
Authors: Hiroshi Yamakawa
Date: 2021-03-06 00:00:00

The vastness of the design space created by the combination of a large number of computational mechanisms, including machine learning, is an obstacle to creating an artificial general intelligence (AGI). Brain-inspired AGI development, in other words, cutting down the design space to look more like a biological brain, which is an existing model of a general intelligence, is a promising plan for solving this problem. However, it is difficult for an individual to design a software program that corresponds to the entire brain because the neuroscientific data required to understand the architecture of the brain are extensive and complicated. The whole-brain architecture approach divides the brain-inspired AGI development process into the task of designing the brain reference architecture (BRA) -- the flow of information and the diagram of corresponding components -- and the task of developing each component using the BRA. This is called BRA-driven development. Another difficulty lies in the extraction of the operating principles necessary for reproducing the cognitive-behavioral function of the brain from neuroscience data. Therefore, this study proposes the Structure-constrained Interface Decomposition (SCID) method, which is a hypothesis-building method for creating a hypothetical component diagram consistent with neuroscientific findings. The application of this approach has begun for building various regions of the brain. Moving forward, we will examine methods of evaluating the biological plausibility of brain-inspired software. This evaluation will also be used to prioritize different computational mechanisms, which should be merged, associated with the same regions of the brain.
Beyond Nyströmformer -- Approximation of self-attention by Spectral Shifting
Authors: Madhusudan Verma
Date: 2021-03-09 00:00:00

Transformer is a powerful tool for many natural language tasks which is based on self-attention, a mechanism that encodes the dependence of other tokens on each specific token, but the computation of self-attention is a bottleneck due to its quadratic time complexity. There are various approaches to reduce the time complexity and approximation of matrix is one such. In Nystr\"omformer, the authors used Nystr\"om based method for approximation of softmax. The Nystr\"om method generates a fast approximation to any large-scale symmetric positive semidefinite (SPSD) matrix using only a few columns of the SPSD matrix. However, since the Nystr\"om approximation is low-rank when the spectrum of the SPSD matrix decays slowly, the Nystr\"om approximation is of low accuracy. Here an alternative method is proposed for approximation which has a much stronger error bound than the Nystr\"om method. The time complexity of this same as Nystr\"omformer which is $O\left({n}\right)$.
Explaining dimensionality reduction results using Shapley values
Authors: Wilson Estécio Marcílio Júnior, Danilo Medeiros Eler
Date: 2021-03-09 00:00:00
Tasks: Clustering, Dimensionality Reduction

Dimensionality reduction (DR) techniques have been consistently supporting high-dimensional data analysis in various applications. Besides the patterns uncovered by these techniques, the interpretation of DR results based on each feature's contribution to the low-dimensional representation supports new finds through exploratory analysis. Current literature approaches designed to interpret DR techniques do not explain the features' contributions well since they focus only on the low-dimensional representation or do not consider the relationship among features. This paper presents ClusterShapley to address these problems, using Shapley values to generate explanations of dimensionality reduction techniques and interpret these algorithms using a cluster-oriented analysis. ClusterShapley explains the formation of clusters and the meaning of their relationship, which is useful for exploratory data analysis in various domains. We propose novel visualization techniques to guide the interpretation of features' contributions on clustering formation and validate our methodology through case studies of publicly available datasets. The results demonstrate our approach's interpretability and analysis power to generate insights about pathologies and patients in different conditions using DR results.
Towards Action Model Learning for Player Modeling
Authors: Abhijeet Krishnan, Aaron Williams, Chris Martens
Date: 2021-03-09 00:00:00
Tasks: model, Sokoban

Player modeling attempts to create a computational model which accurately approximates a player's behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player's cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.
The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning
Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens
Date: 2021-03-09 00:00:00
Tasks: Multi-agent Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds
Authors: Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
Date: 2021-03-09 00:00:00
Proceeding: ICLR 2021 1
Tasks: Off-policy evaluation, Open-Ended Question Answering, Uncertainty Quantification

Off-policy evaluation (OPE) is the task of estimating the expected reward of a given policy based on offline data previously collected under different policies. Therefore, OPE is a key step in applying reinforcement learning to real-world domains such as medical treatment, where interactive data collection is expensive or even unsafe. As the observed data tends to be noisy and limited, it is essential to provide rigorous uncertainty quantification, not just a point estimation, when applying OPE to make high stakes decisions. This work considers the problem of constructing non-asymptotic confidence intervals in infinite-horizon off-policy evaluation, which remains a challenging open question. We develop a practical algorithm through a primal-dual optimization-based approach, which leverages the kernel Bellman loss (KBL) of Feng et al.(2019) and a new martingale concentration inequality of KBL applicable to time-dependent data with unknown mixing conditions. Our algorithm makes minimum assumptions on the data and the function class of the Q-function, and works for the behavior-agnostic settings where the data is collected under a mix of arbitrary unknown behavior policies. We present empirical results that clearly demonstrate the advantages of our approach over existing methods.
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Authors: Louis Faury, Yoan Russac, Marc Abeille, Clément Calauzènes
Date: 2021-03-09 00:00:00

Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity. In this paper we study GLBs in non-stationary environments, characterized by a general metric of non-stationarity known as the variation-budget or \emph{parameter-drift}, denoted $B_T$. While previous attempts have been made to extend LB algorithms to this setting, they overlook a salient feature of GLBs which flaws their results. In this work, we introduce a new algorithm that addresses this difficulty. We prove that under a geometric assumption on the action set, our approach enjoys a $\tilde{\mathcal{O}}(B_T^{1/3}T^{2/3})$ regret bound. In the general case, we show that it suffers at most a $\tilde{\mathcal{O}}(B_T^{1/5}T^{4/5})$ regret. At the core of our contribution is a generalization of the projection step introduced in Filippi et al. (2010), adapted to the non-stationary nature of the problem. Our analysis sheds light on central mechanisms inherited from the setting by explicitly splitting the treatment of the learning and tracking aspects of the problem.
Scalable Online Recurrent Learning Using Columnar Neural Networks
Authors: Khurram Javed, Martha White, Rich Sutton
Date: 2021-03-09 00:00:00
Tasks: Meta-Learning

Structural credit assignment for recurrent learning is challenging. An algorithm called RTRL can compute gradients for recurrent networks online but is computationally intractable for large networks. Alternatives, such as BPTT, are not online. In this work, we propose a credit-assignment algorithm -- \algoname{} -- that approximates the gradients for recurrent learning in real-time using $O(n)$ operations and memory per-step. Our method builds on the idea that for modular recurrent networks, composed of columns with scalar states, it is sufficient for a parameter to only track its influence on the state of its column. We empirically show that as long as connections between columns are sparse, our method approximates the true gradient well. In the special case when there are no connections between columns, the $O(n)$ gradient estimate is exact. We demonstrate the utility of the approach for both recurrent state learning and meta-learning by comparing the estimated gradient to the true gradient on a synthetic test-bed.
Universal Approximation of Residual Flows in Maximum Mean Discrepancy
Authors: Zhifeng Kong, Kamalika Chaudhuri
Date: 2021-03-10 00:00:00
Proceeding: ICML Workshop INNF 2021 7

Normalizing flows are a class of flexible deep generative models that offer easy likelihood computation. Despite their empirical success, there is little theoretical understanding of their expressiveness. In this work, we study residual flows, a class of normalizing flows composed of Lipschitz residual blocks. We prove residual flows are universal approximators in maximum mean discrepancy. We provide upper bounds on the number of residual blocks to achieve approximation under different assumptions.
Reframing Neural Networks: Deep Structure in Overcomplete Representations
Authors: Calvin Murdock, George Cazenavette, Simon Lucey
Date: 2021-03-10 00:00:00
Tasks: Adversarial Robustness, Model Selection, Representation Learning

In comparison to classical shallow representation learning techniques, deep neural networks have achieved superior performance in nearly every application benchmark. But despite their clear empirical advantages, it is still not well understood what makes them so effective. To approach this question, we introduce deep frame approximation: a unifying framework for constrained representation learning with structured overcomplete frames. While exact inference requires iterative optimization, it may be approximated by the operations of a feed-forward deep neural network. We indirectly analyze how model capacity relates to frame structures induced by architectural hyperparameters such as depth, width, and skip connections. We quantify these structural differences with the deep frame potential, a data-independent measure of coherence linked to representation uniqueness and stability. As a criterion for model selection, we show correlation with generalization error on a variety of common deep network architectures and datasets. We also demonstrate how recurrent networks implementing iterative optimization algorithms can achieve performance comparable to their feed-forward approximations while improving adversarial robustness. This connection to the established theory of overcomplete representations suggests promising new directions for principled deep network architecture design with less reliance on ad-hoc engineering.
BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks
Authors: Lyle Regenwetter, Brent Curry, Faez Ahmed
Date: 2021-03-10 00:00:00
Tasks: BIG-bench Machine Learning, Design Synthesis, Dimensionality Reduction, General Classification, Image Generation

In this paper, we present "BIKED," a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset, then highlight some prominent research questions that BIKED can help address. Of these questions, we further explore the following in detail: 1) Are there prominent gaps in the current bicycle market and design space? We explore the design space using unsupervised dimensionality reduction methods. 2) How does one identify the class of a bicycle and what factors play a key role in defining it? We address the bicycle classification task by training a multitude of classifiers using different forms of design data and identifying parameters of particular significance through permutation-based interpretability analysis. 3) How does one synthesize new bicycles using different representation methods? We consider numerous machine learning methods to generate new bicycle models as well as interpolate between and extrapolate from existing models using Variational Autoencoders. The dataset and code are available at http://decode.mit.edu/projects/biked/.