GenAI4ELab/papercli-papers huggingface dataset:
Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract)
Authors: Alexandru Lopotenco; Ian Tong Pan; Jack Zhang; Guan Xiong Qiao
Venue: AAAI 2024 (main)

Abstract: Unsupervised learning methods such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoding are regularly used in dimensionality reduction within the statistical learning scene. However, despite a pivot toward fairness and explainability in machine learning over the past few years, there have been few rigorous attempts toward a generalized framework of fair and explainable representation learning. Our paper explores the possibility of such a framework that leverages maximum mean discrepancy to remove information derived from a protected class from generated representations. For the optimization, we introduce a binary search component to optimize the Lagrangian coefficients. We present rigorous mathematical analysis and experimental results of our framework applied to t-SNE.
Optimizing Recall in Deep Graph Hashing Framework for Item Retrieval (Student Abstract)
Authors: Fangyuan Luo; Jun Wu
Venue: AAAI 2024 (main)

Abstract: Hashing-based recommendation (HR) methods, whose core idea is mapping users and items into hamming space, are common practice to improve item retrieval efficiency. However, existing HR fails to align optimization objective (i.e., Bayesian Personalized Ranking) and evaluation metric (i.e., Recall), leading to suboptimal performance. In this paper, we propose a smooth recall loss (termed as SRLoss), which targets Recall as the optimization objective. Due to the existence of discrete constraints, the optimization problem is NP-hard. To this end, we propose an approximation-adjustable gradient estimator to solve our problem. Experimental Results demonstrate the effectiveness of our proposed method.
Research of Event Reconstruct Based on Multi-View Contrastive Learning (Student Abstract)
Authors: Yuefeng Ma; Zhongchao He; Shumei Wang
Venue: AAAI 2024 (main)

Abstract: The proliferation of social media exacerbates information fragmentation, posing challenges to understanding public events. We address the problem of event reconstruction with a novel Multi-view Contrast Event Reconstruction (MCER) model. MCER maximizes feature dissimilarity between different views of the same event using contrastive learning, while minimizing mutual information between distinct events. This aggregates fragmented views to reconstruct comprehensive event representations. MCER employs momentum and weight-sharing encoders in a three-tower architecture with supervised contrastive loss for multi-view representation learning. Due to the scarcity of multi-view public datasets, we construct a new Mul-view-data benchmark.Experiments demonstrate MCER’s superior performance on public data and our Mul-view-data, significantly outperforming selfsupervised methods by incorporating supervised contrastive techniques. MCER advances multi-view representation learning to counter information fragmentation and enable robust event understanding.
Graph Clustering Methods Derived from Column Subset Selection (Student Abstract)
Authors: Wei Mao; Guihong Wan; Haim Schweitzer
Venue: AAAI 2024 (main)

Abstract: Spectral clustering is a powerful clustering technique. It leverages the spectral properties of graphs to partition data points into meaningful clusters. The most common criterion for evaluating multi-way spectral clustering is NCut. Column Subset Selection is an important optimization technique in the domain of feature selection and dimension reduction which aims to identify a subset of columns of a given data matrix that can be used to approximate the entire matrix. We show that column subset selection can be used to compute spectral clustering and use this to obtain new graph clustering algorithms.
Fast and Knowledge-Free Deep Learning for General Game Playing (Student Abstract)
Authors: Michał Maras; Michał Kępa; Jakub Kowalski; Marek Szykuła
Venue: AAAI 2024 (main)

Abstract: We develop a method of adapting the AlphaZero model to General Game Playing (GGP) that focuses on faster model generation and requires less knowledge to be extracted from the game rules. The dataset generation uses MCTS playing instead of self-play; only the value network is used, and attention layers replace the convolutional ones. This allows us to abandon any assumptions about the action space and board topology. We implement the method within the Regular Boardgames GGP system and show that we can build models outperforming the UCT baseline for most games efficiently.
Towards Robustness to Natural Variations and Distribution Shift (Student Abstract)
Authors: Josué Martínez-Martínez; Olivia Brown; Rajmonda Caceres
Venue: AAAI 2024 (main)

Abstract: This research focuses on improving the robustness of machine learning systems to natural variations and distribution shifts. A design trade space is presented, and various methods are compared, including adversarial training, data augmentation techniques, and novel approaches inspired by model-based robust optimization formulations.
Topological and Node Noise Filtering on 3D Meshes Using Graph Neural Networks (Student Abstract)
Authors: Vladimir Mashurov; Natalia Semenova
Venue: AAAI 2024 (main)

Abstract: Topological and node noise filtration are typically considered separately. Graph Neural Networks (GNN) are commonly used for node noise filtration, as they offer high efficiency and low exploitation costs. This paper explores the solution of joint node and topological noise filtration through the use of graph neural networks. Since treating a 3D mesh as a graph is challenging, an indicator function grid representation is employed as input for GNNs to perform the joint filtering. The resulting machine learning model is inspired by point cloud to mesh reconstruction algorithms and demonstrates low computational requirements during inference, producing successful results for smooth, watertight 3D models.
Enhanced Optical Character Recognition by Optical Sensor Combined with BERT and Cosine Similarity Scoring (Student Abstract)
Authors: Woohyeon Moon; Sarvar Nengroo; Taeyoung Kim; Jihui Lee; Seungah Son; Dongsoo Har
Venue: AAAI 2024 (main)

Abstract: Optical character recognition(OCR) is the technology to identify text characters embedded within images. Conventional OCR models exhibit performance degradation when performing with noisy images. To solve this problem, we propose a novel model, which combines computer vision using optical sensor with natural language processing by bidirectional encoder representations from transformers(BERT) and cosine similarity scoring. The proposed model uses a confidence rate to determine whether to utilize optical sensor alone or BERT/cosine similarity scoring combined with the optical sensor. Experimental results show that the proposed model outperforms approximately 4.34 times better than the conventional OCR.
Several Stories about High-Multiplicity EFx Allocation (Student Abstract)
Authors: Nikita Morozov; Artur Ignatiev; Yuriy Dementiev
Venue: AAAI 2024 (main)

Abstract: Fair division is a topic that has significant social and industrial value. In this work, we study allocations that simultaneously satisfy definitions of fairness and efficiency: EFx and PO. First, we prove that the problem of finding such allocations is NP-hard for two agents. Then, we propose a concept for an ILP-based solving algorithm, the running time of which depends on the number of EFx allocations. We generate input data and analyze algorithm's running time based on the results obtained.
An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract)
Authors: Christine Mwase; Albert Njoroge Kahira; Zhuo Zou
Venue: AAAI 2024 (main)

Abstract: Deep learning (DL), despite its success in various fields, remains expensive and inaccessible to many due to its need for powerful supercomputing and high-end GPUs. This study explores alternative computing infrastructure and methods for distributed DL on low-energy, low-cost devices. We experiment on Raspberry Pi 4 devices with ARM Cortex-A72 processors and train a ResNet-18 model on the CIFAR-10 dataset. Our findings reveal limitations and opportunities for future optimizations, paving the way for a DL toolset for low-energy edge devices.
SkillCLIP: Skill Aware Modality Fusion Visual Question Answering (Student Abstract)
Authors: Atharva Naik; Yash Parag Butala; Navaneethan Vaikunthan; Raghav Kapoor
Venue: AAAI 2024 (main)

Abstract: When humans are posed with a difficult problem, they often approach it by identifying key skills, honing them, and finally effectively combining them. We propose a novel method and apply it for the VizWiz VQA task to predict the visual skills needed to answer a question, and leverage expert modules to produce intermediary outputs and fuse them in a skill-aware manner. Unlike prior works in visual question-answering (VQA) that use intermediate outputs such as detected objects and Optical Character Recognition (OCR), our approach explicitly guides the model with a skill embedding on what to focus on. While our results show that using skill-aware fusion outperforms skill-unaware models for only a subset of questions, we believe our results provide interesting directions for future work. We also release our code, model, and illustrative demonstrations for future research purposes.
MaxEnt Loss: Calibrating Graph Neural Networks under Out-of-Distribution Shift (Student Abstract)
Authors: Dexter Neo
Venue: AAAI 2024 (main)

Abstract: We present a new, simple and effective loss function for calibrating graph neural networks (GNNs). Miscalibration is the problem whereby a model's probabilities does not reflect it's correctness, making it difficult and possibly dangerous for real-world deployment. We compare our method against other baselines on a novel ID and OOD graph form of the Celeb-A faces dataset. Our findings show that our method improves calibration for GNNs, which are not immune to miscalibration in-distribution (ID) and out-of-distribution (OOD). Our code is available for review at https://github.com/dexterdley/CS6208/tree/main/Project.
Welfare Maximization in Perpetual Voting (Student Abstract)
Authors: Tzeh Yuan Neoh; Nicholas Teh
Venue: AAAI 2024 (main)

Abstract: We study the computational problems associated with maximizing various welfare objectives—namely utilitarian welfare, egalitarian welfare, and Nash welfare—in perpetual voting, a sequential collective decision-making framework. Prior work look into notions of fairness over time and study extensions of single-round voting rules to the multi-round setting.
We show that while a utilitarian-welfare maximizing outcome can be computed efficiently, an outcome that maximizes egalitarian or Nash welfare is computationally intractable, even in the case of two candidates. We complement this by showing that maximizing egalitarian welfare is fixed-parameter tractable in the number of agents, and maximizing egalitarian or Nash welfare is W[2]-hard and slicewise polynomial in the number of timesteps. We also provide an approximation algorithm for maximizing egalitarian welfare and study strategyproofness with respect to these welfare objectives. Finally, we show that a simple greedy algorithm can achieve approximate proportionality in this setting.
When Sparse Graph Representation Learning Falls into Domain Shift: Data Augmentation for Cross-Domain Graph Meta-Learning (Student Abstract)
Authors: Simin Niu; Xun Liang; Sensen Zhang; Shichao Song; Xuan Zhang; Xiaoping Zhou
Venue: AAAI 2024 (main)

Abstract: Cross-domain Graph Meta-learning (CGML) has shown its promise, where meta-knowledge is extracted from few-shot graph data in multiple relevant but distinct domains. However, several recent efforts assume target data available, which commonly does not established in practice. In this paper, we devise a novel Cross-domain Data Augmentation for Graph Meta-Learning (CDA-GML), which incorporates the superiorities of CGML and Data Augmentation， has addressed intractable shortcomings of label sparsity, domain shift, and the absence of target data simultaneously. Specifically, our method simulates instance-level and task-level domain shift to alleviate the cross-domain generalization issue in conventional graph meta-learning. Experiments show that our method outperforms the existing state-of-the-art methods.
Target-Free Domain Adaptation through Cross-Adaptation (Student Abstract)
Authors: Aleksander Obuchowski; Barbara Klaudel; Piotr Frąckowski; Sebastian Krajna; Wasyl Badyra; Michał Czubenko; Zdzisław Kowalczuk
Venue: AAAI 2024 (main)

Abstract: The population characteristics of the datasets related to the same task may vary significantly and merging them may harm performance. In this paper, we propose a novel method of domain adaptation called "cross-adaptation". It allows for implicit adaptation to the target domain without the need for any labeled examples across this domain. We test our approach on 9 datasets for SARS-CoV-2 detection from complete blood count from different hospitals around the world. Results show that our solution is universal with respect to various classification algorithms and allows for up to a 10pp increase in F1 score on average.
Large Language Models as Planning Domain Generators (Student Abstract)
Authors: James Oswald; Kavitha Srinivas; Harsha Kokel; Junkyu Lee; Michael Katz; Shirin Sohrabi
Venue: AAAI 2024 (main)

Abstract: The creation of planning models, and in particular domain
models, is among the last bastions of tasks that require exten-
sive manual labor in AI planning; it is desirable to simplify
this process for the sake of making planning more accessi-
ble. To this end, we investigate whether large language mod-
els (LLMs) can be used to generate planning domain models
from textual descriptions. We propose a novel task for this
as well as a means of automated evaluation for generated do-
mains by comparing the sets of plans for domain instances.
Finally, we perform an empirical analysis of 7 large language
models, including coding and chat models across 9 different
planning domains. Our results show that LLMs, particularly
larger ones, exhibit some level of proficiency in generating
correct planning domains from natural language descriptions
A Wireframe-Based Approach for Classifying and Acquiring Proficiency in the American Sign Language (Student Abstract)
Authors: Dylan Pallickara; Sarath Sreedharan
Venue: AAAI 2024 (main)

Abstract: We describe our methodology for classifying ASL (American Sign Language) gestures. Rather than operate directly on raw images of hand gestures, we extract coor-dinates and render wireframes from individual images to construct a curated training dataset. This dataset is then used in a classifier that is memory efficient and provides effective performance (94% accuracy). Because we con-struct wireframes that contain information about several angles in the joints that comprise hands, our methodolo-gy is amenable to training those interested in learning ASL by identifying targeted errors in their hand gestures.
Neuroevolution of a Multi-Generator GAN (Student Abstract)
Authors: Suraj Pandey
Venue: AAAI 2024 (main)

Abstract: Evolutionary Algorithms (EA) have been leveraged to tackle the challenges faced while using GANs such as mode collapse, vanishing gradient, latent space search, etc. However, the existing techniques of using EA with GANs operate backpropagation and EA in isolation from each other, leaving ample room for further exploration. This paper creates a collaborative bridge between EA and GANs by exploring a neuroevolution method for utilising both EA and backpropagation-based optimisation, simultaneously, for a multi-generator GAN architecture. Experiments conducted using a standard dataset with variants of the proposed method highlight the towering impact of each of the components involved in the proposed method.
Graph Anomaly Detection with Diffusion Model-Based Graph Enhancement (Student Abstract)
Authors: Shikang Pang; Chunjing Xiao; Wenxin Tai; Zhangtao Cheng; Fan Zhou
Venue: AAAI 2024 (main)

Abstract: Graph anomaly detection has gained significant research interest across various domains. Due to the lack of labeled data, contrastive learning has been applied in detecting anomalies and various scales of contrastive strategies have been initiated. However, these methods might force two instances (e.g., node-level and subgraph-level representations) with different category labels to be consistent during model training, which can adversely impact the model robustness. To tackle this problem, we present a novel contrastive learning framework with the Diffusion model-based graph Enhancement module for Graph Anomaly Detection, DEGAD. In this framework, we design a diffusion model-based graph enhancement module to manipulate neighbors to generate enhanced graphs, which can efficiently alleviate the inconsistent problem. Further, based on the enhanced graphs, we present a multi-scale contrastive module to discriminate anomalies. Experimental results demonstrate the superiority of our model.
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)
Authors: Bumgeun Park; Taeyoung Kim; Quoc-Vinh Lai-Dang; Dongsoo Har
Venue: AAAI 2024 (main)

Abstract: Efficient exploration for an agent is challenging in reinforcement learning (RL). In this paper, a novel actor-critic framework namely virtual action actor-critic (VAAC), is proposed to address the challenge of efficient exploration in RL. This work is inspired by humans' ability to imagine the potential outcomes of their actions without actually taking them. In order to emulate this ability, VAAC introduces a new actor called virtual actor (VA), alongside the conventional actor-critic framework. Unlike the conventional actor, the VA takes the virtual action to anticipate the next state without interacting with the environment. With the virtual policy following a Gaussian distribution, the VA is trained to maximize the anticipated novelty of the subsequent state resulting from a virtual action. If any next state resulting from available actions does not exhibit high anticipated novelty, training the VA leads to an increase in the virtual policy entropy. Hence, high virtual policy entropy represents that there is no room for exploration. The proposed VAAC aims to maximize a modified Q function, which combines cumulative rewards and the negative sum of virtual policy entropy. Experimental results show that the VAAC improves the exploration performance compared to existing algorithms.
Skip-GANomaly++: Skip Connections and Residual Blocks for Anomaly Detection (Student Abstract)
Authors: June-Young Park; Jae-Ryung Hong; Min-Hye Kim; Tae-Joon Kim
Venue: AAAI 2024 (main)

Abstract: Anomaly detection is a critical task across various domains. Fundamentally, anomaly detection models offer methods to identify unusual patterns that do not align with expected behaviors. Notably, in the medical field, detecting anomalies in medical imagery or biometrics can facilitate early diagnosis of diseases. Consequently, we propose the Skip-GANomaly++ model, an enhanced and more efficient version of the conventional anomaly detection models. The proposed model's performance was evaluated through comparative experiments. Experimental results demonstrated superior performance across most classes compared to the previous models.
QuickRender: A Photorealistic Procedurally Generated Dataset with Applications to Super Resolution (Student Abstract)
Authors: Morgan Payette; Charlotte Curtis
Venue: AAAI 2024 (main)

Abstract: Rendering of complex scenes from software such as Blender is time consuming,  but corresponding auxiliary data such as depth or object segmentation maps are relatively fast to generate. The auxiliary data also provides a wealth of information for tasks such as optical flow prediction.

In this paper we present the QuickRender dataset, a collection of procedurally generated scenes rendered into over 5,000 sequential image triplets along with accompanying auxiliary data. The goal of this dataset is to provide a diversity of scenes and motion while maintaining realistic behaviours. A sample application using this dataset to perform single image super resolution is also presented.

The dataset and related source code can be found at https://github.com/MP-mtroyal/MetaSRGAN.
Knowledge Transfer via Compact Model in Federated Learning (Student Abstract)
Authors: Jiaming Pei; Wei Li; Lukun Wang
Venue: AAAI 2024 (main)

Abstract: Communication overhead remains a significant challenge in federated learning due to frequent global model updates. Essentially, the update of the global model can be viewed as knowledge transfer. We aim to transfer more knowledge through a compact model while reducing communication overhead. In our study, we introduce a federated learning framework where clients pre-train large models locally and the server initializes a compact model to communicate. This compact model should be light in size but still have enough knowledge to refine the global model effectively. We facilitate the knowledge transfer from local to global models based on pre-training outcomes. Our experiments show that our approach significantly reduce communication overhead without sacrificing accuracy.
HyperCube: Implicit Field Representations of Voxelized 3D Models (Student Abstract)
Authors: Magdalena Proszewska; Marcin Mazur; Tomasz Trzciński; Przemysław Spurek
Venue: AAAI 2024 (main)

Abstract: Implicit field representations offer an effective way of generating 3D object shapes. They leverage an implicit decoder (IM-NET) trained to take a 3D point coordinate concatenated with a shape encoding and to output a value indicating whether the point is outside the shape. This approach enables the efficient rendering of visually plausible objects but also has some significant limitations, resulting in a cumbersome training procedure and empty spaces within the rendered mesh. In this paper, we introduce a new HyperCube architecture based on interval arithmetic that enables direct processing of 3D voxels, trained using a hypernetwork paradigm to enforce model convergence. The code is available at https://github.com/mproszewska/hypercube.
Learning Random Noise Salient Feature Fusion Siamese Network for Low-Resolution Object Tracking (Student Abstract)
Authors: Md Maklachur Rahman; Tracy Hammond
Venue: AAAI 2024 (main)

Abstract: Despite Siamese trackers’ substantial potential, they offer sub-optimal tracking performance in low-resolution (LR) contexts. We introduce a Random Noise Salient Feature Fusion Learning Network to address this issue. This method integrates random noise-infused feature maps into a similaritylearning matching model. This integration acts as an effective regularization technique, enhancing the network’s generalization capabilities in LR environments. Additionally, by integrating attention mechanisms, we enhance the discriminative ability of the network, assigning more weights to important features. This directs the network’s focus toward the most salient regions of the feature map, ensuring improved accuracy without a significant increase in parameter overhead, and maintaining a high operating speed. To validate the effectiveness of our method, we performed qualitative and quantitative comparisons with state-of-the-art (SOTA) trackers.
DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)
Authors: Aryaman Rao; Parth Singh; Dinesh Kumar Vishwakarma; Mukesh Prasad
Venue: AAAI 2024 (main)

Abstract: Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.