bluuebunny/crossref_metadata_2025 huggingface dataset:
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling
Authors: Wang Jixuan , Wei Kai , Radfar Martin , Zhang Weiwei , Chung Clement
Published: September 2022

We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling. Specifically, we encode syntactic knowledge into the Transformer encoder by jointly training it to predict syntactic parse ancestors and part-of-speech of each token via multi-task learning. Our model is based on self-attention and feed-forward layers and does not require external syntactic information to be available at inference time. Experiments show that on two benchmark datasets, our models with only two Transformer encoder layers achieve state-of-the-art results. Compared to the previously best performed model without pre-training, our models achieve absolute F1 score and accuracy improvement of 1.59 % and 0.85 % for slot filling and intent detection on the SNIPS dataset, respectively. Our models also achieve absolute F1 score and accuracy improvement of 0.1 % and 0.34 % for slot filling and intent detection on the ATIS dataset, respectively, over the previously best performed model. Furthermore, the visualization of the self-attention weights illustrates the benefits of incorporating syntactic information during training.
On Scalar Embedding of Relative Positions in Attention Models
Authors: Wu Junshuang , Zhang Richong , Mao Yongyi , Chen Junfan
Published: September 2022

Attention with positional encoding has been demonstrated as a powerful component in modern neural network models, such as transformers. However, why positional encoding works well in attention models remains largely unanswered. In this paper, we study the scalar relative positional encoding (SRPE) proposed in the T5 transformer. Such an encoding method has two features. First, it uses a scalar to embed relative positions. Second, the relative positions are bucketized using a fixed heuristic algorithm, and positions in the same bucket share the same embedding. In this work, we show that SRPE in attention has an elegant probabilistic interpretation. More specifically, the positional encoding serves to produce a prior distribution for the attended positions. The resulting attentive distribution can be viewed as a posterior distribution of the attended position given the observed input sequence. Furthermore, we propose a new SRPE (AT5) that adopts a learnable bucketization protocol and automatically adapts to the dependency range specific to the learning task. Empirical studies show that the AT5 achieves superior performance than the T5's SRPE.
Does Head Label Help for Long-Tailed Multi-Label Text Classification
Authors: Xiao Lin , Zhang Xiangliang , Jing Liping , Huang Chi , Song Mingyang
Published: September 2022

Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large fraction of labels are associated with a small number of documents (a.k.a. tail labels). To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels. The meta-knowledge is the mapping from few-shot network parameters to many-shot network parameters, which aims to promote the generalizability of tail classifiers. Extensive experimental results on three benchmark datasets demonstrate that HTTN consistently outperforms the state-of-the-art methods. The code and hyper-parameter settings are released for reproducibility.
Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning
Authors: Xie Binbin , Su Jinsong , Ge Yubin , Li Xiang , Cui Jianwei , Yao Junfeng , Wang Bin
Published: September 2022

Code generation aims to automatically generate a piece of code given an input natural language utterance. Currently, among dominant models, it is treated as a sequence-to-tree task, where a decoder outputs a sequence of actions corresponding to the pre-order traversal of an Abstract Syntax Tree. However, such a decoder only exploits the pre-order traversal based preceding actions, which are insufficient to ensure correct action predictions. In this paper, we first throughly analyze the context modeling difference between neural code generation models with different traversals based decodings (preorder traversal vs breadth-first traversal), and then propose to introduce a mutual learning framework to jointly train these models. Under this framework, we continuously enhance both two models via mutual distillation, which involves synchronous executions of two one-to-one knowledge transfers at each training step. More specifically, we alternately choose one model as the student and the other as its teacher, and require the student to fit the training data and the action prediction distributions of its teacher. By doing so, both models can fully absorb the knowledge from each other and thus could be improved simultaneously. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness of our approach. We release our
code at https://github.com/DeepLearnXMU/CGML.
Contrastive Triple Extraction with Generative Transformer
Authors: Ye Hongbin , Zhang Ningyu , Deng Shumin , Chen Mosha , Tan Chuanqi , Huang Fei , Chen Huajun
Published: September 2022

Triple extraction is an essential task in information extraction for natural language processing and knowledge graph construction. In this paper, we revisit the end-to-end triple extraction task for sequence generation. Since generative triple extraction may struggle to capture long-term dependencies and generate unfaithful triples, we introduce a novel model, contrastive triple extraction with a generative transformer. Specifically, we introduce a single shared transformer module for encoder-decoder-based generation. To generate faithful results, we propose a novel triplet contrastive training object. Moreover, we introduce two mechanisms to further improve model performance (i.e., batch-wise dynamic attention-masking and triple-wise calibration). Experimental results on three datasets (i.e., NYT, WebNLG, and MIE) show that our approach achieves better performance than that of baselines.
Simpson's Bias in NLP Training
Authors: Yuan Fei , Zhang Longtu , Bojun Huang , Liang Yaobo
Published: September 2022

In most machine learning tasks, we evaluate a model M on a given data population S by measuring a population-level metric F(S;M). Examples of such evaluation metric F include precision/recall for (binary) recognition, the F1 score for multi-class classification, and the BLEU metric for language generation. On the other hand, the model M is trained by optimizing a sample-level loss G(S_t; M) at each learning step t, where S_t is a subset of S (a.k.a. the mini-batch). Popular choices of G include cross-entropy loss, the Dice loss, and sentence-level BLEU scores. A fundamental assumption behind this paradigm is that the mean value of the sample-level loss G, if averaged over all possible samples, should effectively represent the population-level metric F of the task, such as, that E[ G(S_t; M) ] ~ F(S; M).

In this paper, we systematically investigate the above assumption in several NLP tasks. We show, both theoretically and experimentally, that some popular designs of the sample-level loss G may be inconsistent with the true population-level metric F of the task, so that models trained to optimize the former can be substantially sub-optimal to the latter, a phenomenon we call it, Simpson's bias, due to its deep connections with the classic paradox known as Simpson's reversal paradox in statistics and social sciences.
Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation
Authors: Zhan Runzhe , Liu Xuebo , Wong Derek F. , Chao Lidia S.
Published: September 2022

Meta-learning has been sufficiently validated to be beneficial for low-resource neural machine translation (NMT). However, we find that meta-trained NMT fails to improve the translation performance of the domain unseen at the meta-training stage. In this paper, we aim to alleviate this issue by proposing a novel meta-curriculum learning for domain adaptation in NMT. During meta-training, the NMT first learns the similar curricula from each domain to avoid falling into a bad local optimum early, and finally learns the curricula of individualities to improve the model robustness for learning domain-specific knowledge. Experimental results on 10 different low-resource domains show that meta-curriculum learning can improve the translation performance of both familiar and unfamiliar domains. All the codes and data are freely available at https://github.com/NLP2CT/Meta-Curriculum.
Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance
Authors: Zhang Dong , Wei Suzhong , Li Shoushan , Wu Hanqian , Zhu Qiaoming , Zhou Guodong
Published: September 2022

Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into pre-defined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have the potential to refine multi-modal representation learning. To deal with this issue, we propose a unified multi-modal graph fusion (UMGF) approach for MNER. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between multi-modal semantic units (words and visual objects). Then, we stack multiple graph-based multi-modal fusion layers that iteratively perform semantic interactions to learn node representations. Finally, we achieve an attention-based multi-modal representation for each word and perform entity labeling with a CRF decoder. Experimentation on the two benchmark datasets demonstrates the superiority of our MNER model.
Accelerating Neural Machine Translation with Partial Word Embedding Compression
Authors: Zhang Fan , Tu Mei , Yan Jinyao
Published: September 2022

Large model size and high computational complexity prevent the neural machine translation (NMT) models from being deployed to low resource devices (e.g. mobile phones). Due to the large vocabulary, a large storage memory is required for the word embedding matrix in NMT models, in the meantime, high latency is introduced when constructing the word probability distribution. Based on reusing the word embedding matrix in the softmax layer, it is possible to handle the two problems brought by large vocabulary at the same time. In this paper, we propose Partial Vector Quantization (P-VQ) for NMT models, which can both compress the word embedding matrix and accelerate word probability prediction in the softmax layer. With P-VQ, the word embedding matrix is split into two low dimensional matrices, namely the shared part and the exclusive part. We compress the shared part by vector quantization and leave the exclusive part unchanged to maintain the uniqueness of each word. For acceleration, in the softmax layer, we replace most of the multiplication operations with the efficient looking-up operations based on our compression to reduce the computational complexity. Furthermore, we adopt curriculum learning and compact the word embedding matrix gradually to improve the compression quality. Experimental results on the Chinese-to-English translation task show that our method can reduce 74.35% of parameters of the word embedding and 74.42% of the FLOPs of the softmax layer. Meanwhile, the average BLEU score on the WMT test sets only drops 0.04.
Continuous Self-Attention Models with Neural ODE Networks
Authors: Zhang Jing , Zhang Peng , Kong Baiwen , Wei Junqiu , Jiang Xin
Published: September 2022

Stacked self-attention models receive widespread attention, due to its ability of capturing global dependency among words. However, the stacking of many layers and components generates huge parameters, leading to low parameter efficiency. In response to this issue, we propose a lightweight architecture named Continuous Self-Attention models with neural ODE networks (CSAODE). In CSAODE, continuous dynamical models (i.e., neural ODEs) are coupled with our proposed self-attention block to form a self-attention ODE solver. This solver continuously calculates and optimizes the hidden states via only one layer of parameters to improve the parameter efficiency. In addition, we design a novel accelerated continuous dynamical model to reduce computing costs, and integrate it in CSAODE. Moreover, since the original self-attention ignores local information, CSAODE makes use of N-gram convolution to encode local representations, and a fusion layer with only two trainable scalars are designed for generating sentence vectors. We perform a series of experiments on text classification, neural language inference (NLI) and text matching tasks. With fewer parameters, CSAODE outperforms state-of-the-art models on text classification tasks (e.g., 1.3% accuracy improved on SUBJ task), and has competitive performances for NLI and text matching tasks as well.
Learning Augmented Methods for Matching: Improving Invasive Species Management and Urban Mobility
Authors: Bjorck Johan , Shi Qinru , Brown-Lima Carrie , Dean Jennifer , Fuller Angela , Gomes Carla
Published: September 2022

With the success of machine learning, integrating learned models into real-world systems has become a critical challenge. Naively applying predictions to combinatorial optimization problems can incur high costs, which has motivated researchers to consider learning augmented algorithms that can make use of faulty or incomplete predictions. Inspired by two matching problems in computational sustainability where data is abundant, we consider the learning augmented min weight matching problem where some nodes are revealed online while others are known a priori, e.g., by being predicted by machine learning. We develop an algorithm that is able to make use of this extra information and provably improves upon pessimistic online algorithms. We evaluate our algorithm on two settings from computational sustainability -- the coordination of unreliable citizen scientists for invasive species management, and the matching between taxis and riders under uncertain trip duration predictions. In both cases, we perform extensive experiments on real-world datasets and find that our method outperforms baselines, showing how learning augmented algorithms can reliably improve solutions for problems in computational sustainability.
Graph Learning for Inverse Landscape Genetics
Authors: Dharangutte Prathamesh , Musco Christopher
Published: September 2022

The problem of inferring unknown graph edges from numerical data at a graph's nodes appears in many forms across machine learning. We study a version of this problem that arises in the field of landscape genetics, where genetic similarity between organisms living in a heterogeneous landscape is explained by a weighted graph that encodes the ease of dispersal through that landscape. Our main contribution is an efficient algorithm for inverse landscape genetics, which is the task of inferring this graph from measurements of genetic similarity at different locations (graph nodes).

Inverse landscape genetics is important in discovering impediments to species dispersal that threaten biodiversity and long-term species survival. In particular, it is widely used to study the effects of climate change and human development. Drawing on influential work that models organism dispersal using graph effective resistances (McRae 2006), we reduce the inverse landscape genetics problem to that of inferring graph edges from noisy measurements of these resistances, which can be obtained from genetic similarity data.

Building on the NeurIPS 2018 work of Hoskins et al. (2018) on learning edges in social networks, we develop an efficient first-order optimization method for solving this problem. Despite its non-convex nature, experiments on synthetic and real genetic data establish that our method provides fast and reliable convergence, significantly outperforming existing heuristics used in the field. By providing researchers with a powerful, general purpose algorithmic tool, we hope our work will have a positive impact on accelerating work on landscape genetics.
Retrieve and Revise: Improving Peptide Identification with Similar Mass Spectra
Authors: Fei Zhengcong
Published: September 2022

Tandem mass spectrometry is an indispensable technology for identification of proteins from complex mixtures. Accurate and sensitive analysis of large amounts of mass spectra data is a principal challenge in proteomics. Conventional deep learning-based peptide identification models usually adopt an encoder-decoder framework and generate target sequence from left to right without fully exploiting the global information. A few recent approaches seek to employ two-pass decoding, yet have limitations when facing the spectra filled with noise. In this paper, we propose a new paradigm for improved peptide identification, which first retrieves a similar mass spectrum from the database as a reference and then revise the matched sequence according to the difference information between the referenced spectrum and current context. The inspiration of design comes that the retrieved peptide-spectrum pair provides a good start point and indirect access to both past and future information, such that each revised amino acid can be produced with better noise perception and global understanding. Moreover, a disturb-based optimization process is introduced to sharpen the attention for difference vector with reinforcement learning before fed to decoder. Experimental results on several public datasets demonstrate that prominent performance boost is obtained with the proposed method. Remarkably, we achieve new state-of-the-art identification results on these datasets.
Fair and Interpretable Algorithmic Hiring using Evolutionary Many Objective Optimization
Authors: Geden Michael , Andrews Joshua
Published: September 2022

Hiring is a high-stakes decision-making process that balances the joint objectives of being fair and accurately selecting the top candidates. The industry standard method employs subject-matter experts to manually generate hiring algorithms; however, this method is resource intensive and finds sub-optimal solutions. Despite the recognized need for algorithmic hiring solutions to address these limitations, no reported method currently supports optimizing predictive objectives while complying to legal fairness standards. We present the novel application of Evolutionary Many-Objective Optimization (EMOO) methods to create the first fair, interpretable, and legally compliant algorithmic hiring approach. Using a proposed novel application of Dirichlet-based genetic operators for improved search, we compare state-of-the-art EMOO models (NSGA-III, SPEA2-SDE, bi-goal evolution) to expert solutions, verifying our results across three real world datasets across diverse organizational positions. Experimental results demonstrate the proposed EMOO models outperform human experts, consistently generate fairer hiring algorithms, and can provide additional lift when removing constraints required for human analysis.
Project RISE: Recognizing Industrial Smoke Emissions
Authors: Hsu Yen-Chia , Huang Ting-Hao (Kenneth) , Hu Ting-Yao , Dille Paul , Prendi Sean , Hoffman Ryan , Tsuhlares Anastasia , Pachuta Jessica , Sargent Randy , Nourbakhsh Illah
Published: September 2022

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for Social Impact.
A Universal 2-state n-action Adaptive Management Solver
Authors: Pascal Luz Valerie , Akian Marianne , Nicol Sam , Chades Iadine
Published: September 2022

In poor data and urgent decision-making applications, managers need to make decisions without complete knowledge of the system dynamics. In biodiversity conservation, adaptive management (AM) is the principal tool for decision-making under uncertainty. AM can be solved using simplified Mixed Observable Markov Decision Processes called hidden model MDPs (hmMDPs) when the unknown dynamics are assumed stationary. hmMDPs provide optimal policies to AM problems by augmenting the MDP state space with an unobservable state variable representing a finite set of predefined models. A drawback in formalising an AM problem is that experts are often solicited to provide this predefined set of models by specifying the transition matrices. Expert elicitation is a challenging and time-consuming process that is prone to biases, and a key assumption of hmMDPs is that the true transition matrix will be included in the candidate model set. We propose an original approach to build a hmMDP with a universal set of predefined models that is capable of solving any 2-state n-action AM problem. Our approach uses properties of the transition matrices to build the model set and is independent of expert input, removing the potential for expert error in the optimal solution. We provide analytical formulations to derive the minimum set of models to include into an hmMDP to solve any AM problems with 2 states and n actions. We assess our universal AM algorithm on two species conservation case studies from Australia and randomly generated problems.
Forecasting Reservoir Inflow via Recurrent Neural ODEs
Authors: Zhou Fan , Li Liang
Published: September 2022

Forecasting reservoir inflow is critical for making many policies, ranging from flood control and agriculture irrigation to water ecology management, hydropower generation, and landslide prevention. Prior studies mainly exploit autoregressive models -- e.g., recurrent neural networks (RNN) and its many variants -- to model the flow time series's temporal pattern. However, existing approaches rely on regular and accurate inflow observations, which either fail to predict multi-scale inflow (e.g., an hour, a day, or a month ahead prediction) or ignore the uncertainty of observations due to confounding factors such as snowmelt and precipitation. To address the limitations, we propose a novel inflow forecasting model by incorporating the uncertainty of the observations into the RNN model and the continuous-time dynamics of the latent states with neural ordinary differential equations (ODE). Our method, called FlowODE, explicitly encodes the stochasticity of hidden conditions in addition to the temporal dependencies among inflow observations. Moreover, FlowODE explores a continuum of layers instead of discrete RNNs to model the hidden states' dynamics, allowing us to infer the inflow at any time horizon flexibly. We conduct extensive experiments on the real-world datasets collected from two large-scale hydropower dams. The results show that our method consistently outperforms previous inflow forecasting models while providing adaptable predictions and a flexible balance between prediction accuracy and computational cost.
Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans
Authors: Altay Fatih , Sánchez Guillermo Ramón , James Yanli , Faraone Stephen V. , Velipasalar Senem , Salekin Asif
Published: September 2022

Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. The most common symptoms include problems with communicating and abstract thinking, as well as disorientation. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training. In this paper, we propose two attention model networks for detecting Alzheimer's disease from MRI images to help early detection efforts at the preclinical stage. We also compare the performance of these two attention network models with a baseline model. Recently available OASIS-3 Longitudinal Neuroimaging, Clinical, and Cognitive Dataset is used to train, evaluate and compare our models. The novelty of this research resides in the fact that we aim to detect Alzheimer's disease when all the parameters, physical assessments, and clinical data state that the patient is healthy and showing no symptoms.
Comparison Lift: Bandit-based Experimentation System for Online Advertising
Authors: Geng Tong , Lin Xiliang , Nair Harikesh S. , Hao Jun , Xiang Bin , Fan Shurui
Published: September 2022

Comparison Lift is an experimentation-as-a-service (EaaS) application for testing online advertising audiences and creatives at JD.com. Unlike many other EaaS tools that focus primarily on fixed sample A/B testing, Comparison Lift deploys a custom bandit-based experimentation algorithm. The advantages of the bandit-based approach are two-fold. First, it aligns the randomization induced in the test with the advertiser’s goals from testing. Second, by adapting experimental design to information acquired during the test, it reduces substantially the cost of experimentation to the advertiser. Since launch in May 2019, Comparison Lift has been utilized in over 1,500 experiments. We estimate that utilization of the product has helped increase click-through rates of participating advertising campaigns by 46% on average. We estimate that the adaptive design in the product has generated 27% more clicks on average during testing compared to a fixed sample A/B design. Both suggest significant value generation and cost savings to advertisers from the product.
Deeplite NeutrinoTM: A BlackBox Framework for Constrained Deep Learning Model Optimization
Authors: Sankaran Anush , Mastropietro Olivier , Saboori Ehsan , Idris Yasser , Sawyer Davis , AskariHemmat MohammadHossein , Boukli Hacene Ghouthi
Published: September 2022

Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers.
While a large-size deeper model could provide competitive accuracy, it creates a lot of logistical challenges and unreasonable resource requirements during development and deployment. This has been one of the key reasons for deep learning models not being excessively used in various production environments, especially in edge devices. There is an immediate requirement for optimizing and compressing these deep learning models, to enable on-device intelligence.
In this research, we introduce a black-box framework, Deeplite Neutrino^{TM} for production-ready optimization of deep learning models. The framework provides an easy mechanism for the end-users to provide constraints such as a tolerable drop in accuracy or target size of the optimized models, to guide the whole optimization process. The framework is easy to include in an existing production pipeline and is available as a Python Package, supporting PyTorch and Tensorflow libraries. The optimization performance of the framework is shown across multiple benchmark datasets and popular deep learning models. Further, the framework is currently used in production and the results and testimonials from several clients are summarized.
Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances
Authors: Wagstaff Kiri , Lu Steven , Dunkel Emily , Grimes Kevin , Zhao Brandon , Cai Jesse , Cole Shoshanna B. , Doran Gary , Francis Raymond , Lee Jake , Mandrake Lukas
Published: September 2022

The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.
Data-Driven Multimodal Patrol Planning for Anti-poaching
Authors: Chen Weizhe , Zhang Weinan , Liu Duo , Li Weiping , Shi Xiaojun , Fang Fei
Published: September 2022

Wildlife poaching is threatening key species that play important roles in the ecosystem. With historical ranger patrol records, it is possible to provide data-driven predictions of poaching threats and plan patrols to combat poaching. However, the patrollers often patrol in a multimodal way, which combines driving and walking. It is a tedious task for the domain experts to manually plan such a patrol and as a result, the planned patrol routes are often far from optimal. In this paper, we propose a data-driven approach for multimodal patrol planning. We first use machine learning models to predict the poaching threats and then use a novel mixed-integer linear programming-based algorithm to plan the patrol route. In a field test focusing on the machine learning prediction result at Jilin Huangnihe National Nature Reserve (HNHR) in December 2019, the rangers found 42 snares, which is significantly higher than the historical record. Our offline experiments show that the resulting multimodal patrol routes can improve the efficiency of patrol and thus they can serve as the basis for future deployment in the field.
Over-MAP: Structural Attention Mechanism and Automated Semantic Segmentation Ensembled for Uncertainty Prediction
Authors: Kantor Charles A. , Boussioux Léonard , Rauby Brice , Talbot Hugues
Published: September 2022

Both theoretical and practical problems in deep learning classification require solutions for assessing uncertainty prediction but current state-of-the-art methods in this area are computationally expensive. In this paper, we propose a new confidence measure dubbed Over-MAP that utilizes a measure of overlap between structural attention mechanisms and segmentation methods, that is of particular interest in accurate fine-grained contexts. We show that this classification confidence increases with the degree of overlap. The associated confidence and identification tools are conceptually simple, efficient, and of high practical interest as they allow for weeding out misleading examples in training data. Our measure is currently deployed in the real-world on widely used platforms to annotate large-scale data efficiently.
SKATE: A Natural Language Interface for Encoding Structured Knowledge
Authors: McFate Clifton , Kalyanpur Aditya , Ferrucci Dave , Bradshaw Andrea , Diertani Ariel , Melville David , Moon Lori
Published: September 2022

In Natural Language (NL) applications, there is often a mismatch between what the NL interface is capable of interpreting and what a lay user knows how to express. This work describes a novel natural language interface that reduces this mismatch by refining natural language input through successive, automatically generated semi-structured templates. In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We also show how SKATE integrates with a neural rule-generation model to interactively suggest and acquire commonsense knowledge. We provide a preliminary coverage analysis of SKATE for the task of story understanding, and then describe a current business use-case of the technology in a restricted domain: COVID-19 policy design.
PoseBlocks: A Toolkit for Creating (and Dancing) with AI
Authors: Jordan Brian , Devasia Nisha , Hong Jenna , Williams Randi , Breazeal Cynthia
Published: September 2022

Body-tracking artificial intelligence (AI) systems like Kinect games, Snapchat Augmented Reality (AR) Lenses, and Instagram AR Filters are some of the most engaging ways students experience AI in their everyday lives. Additionally, many students have existing interests in physical hobbies like sports and dance. In this paper, we present PoseBlocks; a suite of block-based programming tools which enable students to build compelling body-interactive AI projects in any web browser, integrating camera/microphone inputs and body-sensing user interactions. To accomplish this, we provide a custom block-based programming environment building on the open source Scratch project, introducing new AI-model-powered blocks supporting body, hand, and face tracking, emotion recognition, and the ability to integrate custom image/pose/audio models from the online transfer learning tool Teachable Machine. We introduce editor functionality such as a project video recorder, pre-computed video loops, and integration with curriculum materials. We discuss deploying this toolkit with an accompanying curriculum in a series of synchronous online pilots with 46 students, aged 9-14. In analyzing class projects and discussions, we find that students learned to design, train, and integrate machine learning models in projects of their own devising while exploring ethical considerations such as stakeholder values and algorithmic bias in their interactive AI systems.
A Highly-Parameterized Ensemble to Play Gin Rummy
Authors: Nagai Masayuki , Shrivastava Kavya , Ta Kien , Bogaerts Steven , Byers Chad
Published: September 2022

This paper describes the design and training of a computer Gin Rummy player. The system includes three main components to make decisions about drawing cards, discarding, and ending the game, with numerous parameters controlling behavior. In particular, an ensemble approach is explored in the discard decision. Finally, three sets of parameter tuning and performance experiments are analyzed.
Designing a Hybrid AI Residency
Authors: Silva Felipe Leno Da , Stanzani Silvio , Fialho Jefferson , Mondadori Jorge , Mazzetto Muriel , Couto Felipe Sanches , Cobe Raphael
Published: September 2022

The industry demand for AI experts raised to unprecedented levels in the last years. However, the increasing demand was not met by the number of skilled professionals in this area. As an effort to mitigate this problem, many companies create AI residency programs to provide in-house practical training. However, we argue that the usual dynamics based on one-on-one mentorship in those programs is very hard to scale and insufficient to meet the demand for AI professionals. In this paper, we describe a hybrid AI residency program that connects educational institutions, partner companies, and prospective residents. This program is designed to be funded by partner companies.Residents are exposed to practical projects of industry interest and are instructed on AI techniques and tools. We describe how we implemented our program, the challenges involved, and the lessons learned after the conclusion of the first residency class. Our program was developed to be inclusive and scalable, and resulted in a high employment rate for our alumni. Furthermore, several partner companies invested in in-house AI teams after the residency, resulting in direct benefits for our local AI community.
A Data-Driven Approach for Gin Rummy Hand Evaluation
Authors: Truong Sang T. , Neller Todd W.
Published: September 2022

We develop a data-driven approach for hand strength evaluation in the game of Gin Rummy. Employing Convolutional Neural Networks, Monte Carlo simulation, and Bayesian reasoning, we compute both offensive and defensive scores of a game state. After only one training cycle, the model was able to make sophisticated and human-like decisions with a 55.4% +/- 0.8% win rate (90% confidence level) against a Simple player.
Teacher Perspectives on How To Train Your Robot: A Middle School AI and Ethics Curriculum
Authors: Williams Randi , Kaputsos Stephen P. , Breazeal Cynthia
Published: September 2022

To enable a diverse citizenry to fully participate in future society, we must prepare all students to construct and critique emerging technologies like Artificial Intelligence (AI). Classrooms are important spaces to teach students these skills, however there are few AI curricula that have been developed for and used by K-12 teachers. We developed the \textit{How to Train Your Robot: AI and Ethics Curriculum} for middle school teachers who want to introduce AI to their students. This paper describes the curriculum and professional development we used to prepare teachers to run a five-day AI course. Before and after they ran the curriculum, we interviewed teachers to understand their opinions on pedagogical approaches to teaching AI, meeting students' needs, and the feasibility of doing the activities in the classroom. Our results indicate that, with appropriate training, even teachers who were new to computer science felt prepared and successfully engaged their students in the topic. We hope our insights will inform future efforts to realize AI education in primary and secondary classrooms.
Perception Beyond Sensors Under Uncertainty
Authors: Itkina Masha
Published: September 2022

My research aims to enable spatiotemporal inference in mobile robot perception systems. Specifically, the proposed thesis presents learning-based approaches to the tasks of behavior prediction and occlusion inference that explicitly account for the associated aleatoric and epistemic uncertainty.
Constraint-Driven Learning of Logic Programs
Authors: Morel Rolf
Published: September 2022

Two fundamental challenges in program synthesis, i.e. learning programs from specifications, are (1) program correctness and (2) search efficiency. We claim logical constraints can address both: (1) by expressing strong requirements on solutions and (2) due to being effective at eliminating non-solutions. When learning from examples, a hypothesis failing on an example means that (a class of) related programs fail as well. We encode these classes into constraints, thereby pruning away many a failing hypothesis. We are expanding this method with failure explanation: identify failing sub-programs the related programs of which can be eliminated as well. In addition to reasoning about examples, programming involves ensuring general properties are not violated. Inspired by the synthesis of functional programs, we intend to encode correctness properties as well as runtime complexity bounds into constraints.
AI for Social Good: Between My Research and the Real World
Authors: Shi Zheyuan Ryan
Published: September 2022

AI for social good (AI4SG) is a research theme that aims to use and advance AI to improve the well-being of society. My work on AI4SG builds a two-way bridge between the research world and the real world. Using my unique experience in food waste and security, I propose applied AI4SG research that directly addresses real-world challenges which have received little attention from the community. Drawing from my experience in various AI4SG application domains, I propose bandit data-driven optimization, the first iterative prediction-prescription framework and a no-regret algorithm PROOF. I will apply PROOF back to my applied work on AI4SG, thereby closing the loop in a single framework.
Multi-agent Reinforcement Learning for Decentralized Coalition Formation Games
Authors: Taywade Kshitija
Published: September 2022

We study the application of multi-agent reinforcement learning for game-theoretical problems. In particular, we are interested in coalition formation problems and their variants such as hedonic coalition formation games (also called hedonic games), matching (a common type of hedonic game), and coalition formation for task allocation. We consider decentralized multi-agent systems where autonomous agents inhabit an environment without any prior knowledge of other agents or the system. We also consider spatial formulations of these problems. Most of the literature for coalition formation problems does not consider these formulations of the problems because it increases computational complexity significantly. We propose novel decentralized heuristic learning
and multi-agent reinforcement learning (MARL) approaches to train agents, and we use game-theoretic evaluation criteria such as optimality, stability, and indices like Shapley value.
Reward based Hebbian Learning in Direct Feedback Alignment (Student Abstract)
Authors: Akella Ashlesha , Singanamalla Sai Kalyan Ranga , Lin Chin-Teng
Published: September 2022

Imparting biological realism during the learning process is gaining attention towards producing computationally efficient algorithms without compromising the performance. Feedback alignment and mirror neuron concept are two such approaches where the feedback weight remains static in the former and update via Hebbian learning in the later. Though these approaches have proven to work efficiently for supervised learning, it remained unknown if the same can be applicable to reinforcement learning applications. Therefore, this study introduces RHebb-DFA where the reward-based Hebbian learning is used to update feedback weights in direct feedback alignment mode. This approach is validated on various Atari games and obtained equivalent performance in comparison with DDQN.
Reducing Neural Network Parameter Initialization Into an SMT Problem (Student Abstract)
Authors: Danesh Mohamad H.
Published: September 2022

Training a neural network (NN) depends on multiple factors, including but not limited to the initial weights. In this paper, we focus on initializing deep NN parameters such that it performs better, comparing to random or zero initialization. We do this by reducing the process of initialization into an SMT solver. Previous works consider certain activation functions on small NNs, however the studied NN is a deep network with different activation functions. Our experiments show that the proposed approach for parameter initialization achieves better performance comparing to randomly initialized networks.
Evaluating Meta-Reinforcement Learning through a HVAC Control Benchmark (Student Abstract)
Authors: Grewal Yashvir S. , De Nijs Frits , Goodwin Sarah
Published: September 2022

Meta-Reinforcement Learning (RL) algorithms promise to leverage prior task experience to quickly learn new unseen tasks. Unfortunately, evaluating meta-RL algorithms is complicated by a lack of suitable benchmarks. In this paper we propose adapting a challenging real-world heating, ventilation and air-conditioning (HVAC) control benchmark for meta-RL. Unlike existing benchmark problems, HVAC control has a broader task distribution, and sources of exogenous stochasticity from price and weather predictions which can be shared across task definitions. This can enable greater differentiation between the performance of current meta-RL approaches, and open the way for future research into algorithms that can adapt to entirely new tasks not sampled from the current task distribution.
RGB-D Scene Recognition based on Object-Scene Relation (Student Abstract)
Authors: Guo Yuhui , Liang Xun
Published: September 2022

We develop a RGB-D scene recognition model based on object-scene relation(RSBR). First learning a Semantic Network in the semantic domain that classifies the label of a scene on the basis of the labels of all object types. Then, we design an Appearance Network in the appearance domain that recognizes the scene according to local captions. We enforce the Semantic Network to guide the Appearance Network in the learning procedure. Based on the proposed RSBR model, we obtain the state-of-the-art results of RGB-D scene recognition on SUN RGB-D and NYUD2 datasets.
Pedestrian's Intention Recognition, Fusion of Handcrafted Features in a Deep Learning Approach
Authors: Hamed Omar , Steinhauer H. Joe
Published: September 2022

The safety of vulnerable road users (VRU) is a major concern for both advanced driver assistance systems (ADAS) and autonomous vehicle manufacturers. To guarantee people safety
on roads, autonomous vehicles must be able to detect the presence of pedestrians, track them, and predict their intention to cross the road. Most of the earlier work on pedestrian intention recognition focused on using either handcrafted features or an end-to-end deep learning approach. In this project, we investigate the impact of fusing handcrafted features
with auto learned features by using a two-stream neural network architecture. Our results show that the combined approach improves the performance. Furthermore, the proposed
method achieved very good results on the JAAD dataset. Depending on whether we considered the immediate frames before the crossing or only half a second before that point, we
received prediction accuracy of 91%, and 84%, respectively.
Dethroning Aristocracy in Graphs via Adversarial Perturbations (Student Abstract)
Authors: Jamadandi Adarsh , Mudenagudi Uma
Published: September 2022

Learning low-dimensional embeddings of graph data in curved Riemannian manifolds has gained traction due to their desirable property of acting as effective geometrical inductive biases. More specifically, models of Hyperbolic geometry such as Poincar\'{e} Ball and Lorentz/Hyperboloid Model have found applications for learning data with hierarchical anatomy. Gromov's hyperbolicity measures whether a graph can be isometrically embedded in hyperbolic space. This paper shows that adversarial attacks that perturb the network structure also affect the hyperbolicity of graphs rendering hyperbolic space less effective for learning low-dimensional node embeddings of the graph. To circumvent this problem, we introduce learning embeddings in pseudo-Riemannian manifolds such as Lorentzian manifolds and show empirically that they are robust to adversarial perturbations. Despite the recent proliferation of adversarial robustness methods in the graph data, this is the first work exploring the relationship between adversarial attacks and hyperbolicity while also providing resolution to navigate such vulnerabilities.
Gradient-Based Localization and Spatial Attention for Confidence Measure in Fine-Grained Recognition using Deep Neural Networks
Authors: Kantor Charles A. , Boussioux Léonard , Rauby Brice , Talbot Hugues
Published: September 2022

Both theoretical and practical problems in deep learning classification benefit from assessing uncertainty prediction. In addition, current state-of-the-art methods in this area are computationally expensive: for example,~\cite{loquercio2020general} is a general method for uncertainty estimation in deep learning that relies on Monte-Carlo sampling. We propose a new, efficient confidence measure later dubbed Over-MAP that utilizes a measure of overlap between structural attention mechanisms and segmentation methods. It does not rely on sampling or retraining. We show that the classification confidence increases with the degree of overlap. The associated confidence and identification tools are conceptually simple, efficient and of high practical interest as they allow for weeding out misleading examples in training data. Our measure is currently deployed in the real-world on widely used platforms to annotate large-scale data efficiently.
Compilation Complexity of Multi-Winner Voting Rules (Student Abstract)
Authors: Karia Neel , Lang Jérôme
Published: September 2022

Compiling the votes of a subelectorate consists of storing the votes of a subset of voters in a compressed form, such that the winners can still be determined when additional votes are included. This leads to the notion of compilation complexity, which has already been investigated for single-winner voting rules. We perform a compilation complexity analysis of several common multi-winner voting rules.