pwc-archive/papers-with-abstracts huggingface dataset:
Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks
Authors: William F. Podlaski, Christian K. Machens
Date: 2023-07-18 00:00:00

Deep feedforward and recurrent rate-based neural networks have become successful functional models of the brain, but they neglect obvious biological details such as spikes and Dale's law. Here we argue that these details are crucial in order to understand how real neural circuits operate. Towards this aim, we put forth a new framework for spike-based computation in low-rank excitatory-inhibitory spiking networks. By considering populations with rank-1 connectivity, we cast each neuron's spiking threshold as a boundary in a low-dimensional input-output space. We then show how the combined thresholds of a population of inhibitory neurons form a stable boundary in this space, and those of a population of excitatory neurons form an unstable boundary. Combining the two boundaries results in a rank-2 excitatory-inhibitory (EI) network with inhibition-stabilized dynamics at the intersection of the two boundaries. The computation of the resulting networks can be understood as the difference of two convex functions and is thereby capable of approximating arbitrary non-linear input-output mappings. We demonstrate several properties of these networks, including noise suppression and amplification, irregular activity and synaptic balance, as well as how they relate to rate network dynamics in the limit that the boundary becomes soft. Finally, while our work focuses on small networks (5-50 neurons), we discuss potential avenues for scaling up to much larger networks. Overall, our work proposes a new perspective on spiking networks that may serve as a starting point for a mechanistic understanding of biological spike-based computation.
Adaptively Optimised Adaptive Importance Samplers
Authors: Carlos A. C. C. Perello, Ömer Deniz Akyildiz
Date: 2023-07-18 00:00:00

We introduce a new class of adaptive importance samplers leveraging adaptive optimisation tools, which we term AdaOAIS. We build on Optimised Adaptive Importance Samplers (OAIS), a class of techniques that adapt proposals to improve the mean-squared error of the importance sampling estimators by parameterising the proposal and optimising the $\chi^2$-divergence between the target and the proposal. We show that a naive implementation of OAIS using stochastic gradient descent may lead to unstable estimators despite its convergence guarantees. To remedy this shortcoming, we instead propose to use adaptive optimisers (such as AdaGrad and Adam) to improve the stability of the OAIS. We provide convergence results for AdaOAIS in a similar manner to OAIS. We also provide empirical demonstration on a variety of examples and show that AdaOAIS lead to stable importance sampling estimators in practice.
Nested stochastic block model for simultaneously clustering networks and nodes
Authors: Nathaniel Josephs, Arash A. Amini, Marina Paez, Lizhen Lin
Date: 2023-07-18 00:00:00
Tasks: Clustering, Stochastic Block Model

We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network. NSBM has several appealing features including the ability to work on unlabeled networks with potentially different node sets, the flexibility to model heterogeneous communities, and the means to automatically select the number of classes for the networks and the number of communities within each network. This is accomplished via a Bayesian model, with a novel application of the nested Dirichlet process (NDP) as a prior to jointly model the between-network and within-network clusters. The dependency introduced by the network data creates nontrivial challenges for the NDP, especially in the development of efficient samplers. For posterior inference, we propose several Markov chain Monte Carlo algorithms including a standard Gibbs sampler, a collapsed Gibbs sampler, and two blocked Gibbs samplers that ultimately return two levels of clustering labels from both within and across the networks. Extensive simulation studies are carried out which demonstrate that the model provides very accurate estimates of both levels of the clustering structure. We also apply our model to two social network datasets that cannot be analyzed using any previous method in the literature due to the anonymity of the nodes and the varying number of nodes in each network.
Estimation of an Order Book Dependent Hawkes Process for Large Datasets
Authors: Luca Mucciante, Alessio Sancetta
Date: 2023-07-18 00:00:00

A point process for event arrivals in high frequency trading is presented. The intensity is the product of a Hawkes process and high dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high dimensional space. The large sample size can be common for high frequency data applications using multiple liquid instruments. Convergence of the algorithm is shown, consistency results under weak conditions is established, and a test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange (NYSE). The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self exciting nature of high frequency trading events.
A Covariate-Adjusted Homogeneity Test with Application to Facial Recognition Accuracy Assessment
Authors: Ngoc-Ty Nguyen, P. Jonathon Phillips, Larry Tang
Date: 2023-07-17 00:00:00
Tasks: Face Recognition

Ordinal scores occur commonly in medical imaging studies and in black-box forensic studies \citep{Phillips:2018}. To assess the accuracy of raters in the studies, one needs to estimate the receiver operating characteristic (ROC) curve while accounting for covariates of raters. In this paper, we propose a covariate-adjusted homogeneity test to determine differences in accuracy among multiple rater groups. We derived the theoretical results of the proposed test and conducted extensive simulation studies to evaluate the finite sample performance of the proposed test. Our proposed test is applied to a face recognition study to identify statistically significant differences among five participant groups.
Learning to Select and Rank from Choice-Based Feedback: A Simple Nested Approach
Authors: Junwen Yang, Yifan Feng
Date: 2023-07-13 00:00:00
Tasks: Combinatorial Optimization

We study a ranking and selection problem of learning from choice-based feedback with dynamic assortments. In this problem, a company sequentially displays a set of items to a population of customers and collects their choices as feedback. The only information available about the underlying choice model is that the choice probabilities are consistent with some unknown true strict ranking over the items. The objective is to identify, with the fewest samples, the most preferred item or the full ranking over the items at a high confidence level. We present novel and simple algorithms for both learning goals. In the first subproblem regarding best-item identification, we introduce an elimination-based algorithm, Nested Elimination (NE). In the more complex subproblem regarding full-ranking identification, we generalize NE and propose a divide-and-conquer algorithm, Nested Partition (NP). We provide strong characterizations of both algorithms through instance-specific and non-asymptotic bounds on the sample complexity. This is accomplished using an analytical framework that characterizes the system dynamics through analyzing a sequence of multi-dimensional random walks. We also establish a connection between our nested approach and the information-theoretic lower bounds. We thus show that NE is worst-case asymptotically optimal, and NP is optimal up to a constant factor. Finally, numerical experiments from both synthetic and real data corroborate our theoretical findings.
Deep Neural Aggregation for Recommending Items to Group of Users
Authors: Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega, Jesús Bobadilla
Date: 2023-07-18 00:00:00
Tasks: Recommendation Systems

Modern society devotes a significant amount of time to digital interaction. Many of our daily actions are carried out through digital means. This has led to the emergence of numerous Artificial Intelligence tools that assist us in various aspects of our lives. One key tool for the digital society is Recommender Systems, intelligent systems that learn from our past actions to propose new ones that align with our interests. Some of these systems have specialized in learning from the behavior of user groups to make recommendations to a group of individuals who want to perform a joint task. In this article, we analyze the current state of Group Recommender Systems and propose two new models that use emerging Deep Learning architectures. Experimental results demonstrate the improvement achieved by employing the proposed models compared to the state-of-the-art models using four different datasets. The source code of the models, as well as that of all the experiments conducted, is available in a public repository.
Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion
Authors: Menghan Wang, Jinming Yang, Yuchen Guo, Yuming Shen, Mengying Zhu, Yanlin Wang
Date: 2023-07-18 00:00:00
Tasks: Multi-Task Learning, Selection bias

User post-click conversion prediction is of high interest to researchers and developers. Recent studies employ multi-task learning to tackle the selection bias and data sparsity problem, two severe challenges in post-click behavior prediction, by incorporating click data. However, prior works mainly focused on pointwise learning and the orders of labels (i.e., click and post-click) are not well explored, which naturally poses a listwise learning problem. Inspired by recent advances on differentiable sorting, in this paper, we propose a novel multi-task framework that leverages orders of user behaviors to predict user post-click conversion in an end-to-end approach. Specifically, we define an aggregation operator to combine predicted outputs of different tasks to a unified score, then we use the computed scores to model the label relations via differentiable sorting. Extensive experiments on public and industrial datasets show the superiority of our proposed model against competitive baselines.
Mixed-initiative Query Rewriting in Conversational Passage Retrieval
Authors: Dayu Yang, Yue Zhang, Hui Fang
Date: 2023-07-17 00:00:00
Tasks: Passage Retrieval, Retrieval

In this paper, we report our methods and experiments for the TREC Conversational Assistance Track (CAsT) 2022. In this work, we aim to reproduce multi-stage retrieval pipelines and explore one of the potential benefits of involving mixed-initiative interaction in conversational passage retrieval scenarios: reformulating raw queries. Before the first ranking stage of a multi-stage retrieval pipeline, we propose a mixed-initiative query rewriting module, which achieves query rewriting based on the mixed-initiative interaction between the users and the system, as the replacement for the neural rewriting method. Specifically, we design an algorithm to generate appropriate questions related to the ambiguities in raw queries, and another algorithm to reformulate raw queries by parsing users' feedback and incorporating it into the raw query. For the first ranking stage of our multi-stage pipelines, we adopt a sparse ranking function: BM25, and a dense retrieval method: TCT-ColBERT. For the second-ranking step, we adopt a pointwise reranker: MonoT5, and a pairwise reranker: DuoT5. Experiments on both TREC CAsT 2021 and TREC CAsT 2022 datasets show the effectiveness of our mixed-initiative-based query rewriting (or query reformulation) method on improving retrieval performance compared with two popular reformulators: a neural reformulator: CANARD-T5 and a rule-based reformulator: historical query reformulator(HQE).
Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems
Authors: Tung Nguyen, Jeffrey Uhlmann
Date: 2023-07-17 00:00:00
Tasks: Fairness, Matrix Completion, Recommendation Systems

In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e.g., AI-based. We demonstrate our approach in the context of blackbox SVD-based matrix-completion methods commonly used in recommender system (RS) applications. We provide empirical results showing that enforcement of unit-consistency and shift-consistency, which have provable RS-relevant properties relating to robustness and fairness, also lead to improved performance according to generic RMSE and MAE performance metrics, irrespective of the initial chosen hyperparameter.
Video Search with CLIP and Interactive Text Query Reformulation
Authors: Jakub Lokoč, Zuzana Vopálková, Patrik Dokoupil, Ladislav Peška
Date: 2023-03-29 00:00:00
Proceeding: International Conference on Multimedia Modeling 2023 3

Nowadays, deep learning based models like CLIP allow simple design of cross-modal video search systems that are able to solve many tasks considered as highly challenging several years ago. In this paper, we analyze a CLIP based search approach that focuses on situations, where users cannot find proper text queries to describe searched video segments. The approach relies on suggestions of classes for displayed intermediate result sets and thus allows users to realize missing words and ideas to describe video frames. This approach is supported with a preliminary study showing potential of the method. Based on the results, we extend a respected known-item search system for the Video Browser Showdown, where more challenging visual known-item search tasks are planned.
Free-Form Multi-Modal Multimedia Retrieval (4MR)
Authors: R Arnold, L Sauter, H Schuldt
Date: 2023-03-29 00:00:00
Proceeding: International Conference on Multimedia Modeling 2023 3
Tasks: Form, Management, Retrieval, Text Retrieval, Video Retrieval

Due to the ever increasing amount of multimedia data, efficient means for multimedia management and retrieval are required. Especially with the rise of deep-learning-based analytics methods, the semantic gap has shrunk considerably, but a human in the loop is still considered mandatory. One of the driving factors of video search is that humans tend to refine their queries after reviewing the results. Hence, the entire process is highly interactive. A natural approach to interactive video search is using textual descriptions of the content of the expected result, enabled by deep learning-based joint visual text co-embedding. In this paper, we present the Multi-Modal Multimedia Retrieval (4MR) system, a novel system inspired by vitrivr, that empowers users with almost entirely free-form query formulation methods. The top-ranked teams of the last few iterations of the Video Browser Showdown have shown that CLIP provides an ideal feature extraction method. Therefore, while 4MR is capable of image and text retrieval as well, for VBS video retrieval is based primarily based on CLIP.
Perfect Match in Video Retrieval
Authors: S Lubos, Massimiliano Rubino, Christian Tautschnig, Markus Tautschnig, Boda Wen, Klaus Schoeffmann, Alexander Felfernig
Date: 2023-03-29 00:00:00
Proceeding: International Conference on Multimedia Modeling 2023 3
Tasks: Retrieval, Video Retrieval

This paper presents the first version of our video search system Perfect Match for the Video Browser Showdown 2023 competition. The system indexes videos from the large V3C video dataset and derives visual content descriptors automatically. Furthermore, it provides an interactive video search user interface (UI), which implements approaches from the domain of critiquing-based recommendation, to enable the user to find the desired video segment as fast as possible.
Llama 2: Open Foundation and Fine-Tuned Chat Models
Authors: Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom
Date: 2023-07-18 00:00:00
Tasks: Arithmetic Reasoning, Code Generation, Math Word Problem Solving, Multiple Choice Question Answering (MCQA), Multi-task Language Understanding, Question Answering, Sentence Completion

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.
Air Traffic Management Using a GPU-Accelerated Genetic Algorithm
Authors: Rahul Rampure, Raghav Tiruvallur, Vybhav Acharya, Shashank Navad, Preethi P
Date: 2023-06-17 00:00:00
Proceeding: Transport and Telecommunication Journal 2023 6
Tasks: GPU, Management, Traffic Prediction

Air traffic management is becoming highly complex with the rapid increase in the number of commercial and cargo flights,leading to increased traffic congestion and flight delays. To mitigate these issues, we present a flight path generation system thatdistributes the aeroplanes across the airspace and imparts minimal delays to the flight if required, thus ensuring that the aircraft followsthe shortest route wherein it encounters the least amount of traffic. We develop a parallel genetic algorithm in CUDA-C with a novelfitness function allowing the system to reach an optimal solution where the air traffic density is minimised. The proposed algorithmwas tested on one day’s domestic flight schedule and achieved an 18% reduction in traffic density, with the flight times and delaysremaining proportional to the data observed in the existing air traffic management system
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
Authors: JianGuo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong
Date: 2023-07-19 00:00:00
Tasks: Conversational Recommendation, Diversity, Few-Shot Learning, Language Modeling, Language Modelling, Natural Language Understanding

Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness. To tackle these issues, we introduce DialogStudio: the largest and most diverse collection of dialogue datasets, unified under a consistent format while preserving their original information. Our collection encompasses data from open-domain dialogues, task-oriented dialogues, natural language understanding, conversational recommendation, dialogue summarization, and knowledge-grounded dialogues, making it an incredibly rich and diverse resource for dialogue research and model training. To further enhance the utility of DialogStudio, we identify the licenses for each dataset, design external knowledge and domain-aware prompts for selected dialogues to facilitate instruction-aware fine-tuning. Furthermore, we develop conversational AI models using the dataset collection, and our experiments in both zero-shot and few-shot learning scenarios demonstrate the superiority of DialogStudio. To improve transparency and support dataset and task-based research, as well as language model pre-training, all datasets, licenses, codes, and models associated with DialogStudio are made publicly accessible\footnote{\url{https://github.com/salesforce/DialogStudio}}.
A Fast and Map-Free Model for Trajectory Prediction in Traffics
Authors: Junhong Xiang, Jingmin Zhang, Zhixiong Nan
Date: 2023-07-19 00:00:00
Tasks: Trajectory Prediction

To handle the two shortcomings of existing methods, (i)nearly all models rely on high-definition (HD) maps, yet the map information is not always available in real traffic scenes and HD map-building is expensive and time-consuming and (ii) existing models usually focus on improving prediction accuracy at the expense of reducing computing efficiency, yet the efficiency is crucial for various real applications, this paper proposes an efficient trajectory prediction model that is not dependent on traffic maps. The core idea of our model is encoding single-agent's spatial-temporal information in the first stage and exploring multi-agents' spatial-temporal interactions in the second stage. By comprehensively utilizing attention mechanism, LSTM, graph convolution network and temporal transformer in the two stages, our model is able to learn rich dynamic and interaction information of all agents. Our model achieves the highest performance when comparing with existing map-free methods and also exceeds most map-based state-of-the-art methods on the Argoverse dataset. In addition, our model also exhibits a faster inference speed than the baseline methods.
LightPath: Lightweight and Scalable Path Representation Learning
Authors: Sean Bin Yang, Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen
Date: 2023-07-19 00:00:00
Tasks: Knowledge Distillation, Relational Reasoning, Representation Learning

Movement paths are used widely in intelligent transportation and smart city applications. To serve such applications, path representation learning aims to provide compact representations of paths that enable efficient and accurate operations when used for different downstream tasks such as path ranking and travel cost estimation. In many cases, it is attractive that the path representation learning is lightweight and scalable; in resource-limited environments and under green computing limitations, it is essential. Yet, existing path representation learning studies focus on accuracy and pay at most secondary attention to resource consumption and scalability. We propose a lightweight and scalable path representation learning framework, termed LightPath, that aims to reduce resource consumption and achieve scalability without affecting accuracy, thus enabling broader applicability. More specifically, we first propose a sparse auto-encoder that ensures that the framework achieves good scalability with respect to path length. Next, we propose a relational reasoning framework to enable faster training of more robust sparse path encoders. We also propose global-local knowledge distillation to further reduce the size and improve the performance of sparse path encoders. Finally, we report extensive experiments on two real-world datasets to offer insight into the efficiency, scalability, and effectiveness of the proposed framework.