pwc-archive/papers-with-abstracts huggingface dataset:
On the Robustness of Fully-Spiking Neural Networks in Open-World Scenarios using Forward-Only Learning Algorithms
Authors: Erik B. Terres-Escudero, Javier Del Ser, Aitor Martínez-Seras, Pablo Garcia-Bringas
Date: 2024-07-19 00:00:00

In the last decade, Artificial Intelligence (AI) models have rapidly integrated into production pipelines propelled by their excellent modeling performance. However, the development of these models has not been matched by advancements in algorithms ensuring their safety, failing to guarantee robust behavior against Out-of-Distribution (OoD) inputs outside their learning domain. Furthermore, there is a growing concern with the sustainability of AI models and their required energy consumption in both training and inference phases. To mitigate these issues, this work explores the use of the Forward-Forward Algorithm (FFA), a biologically plausible alternative to Backpropagation, adapted to the spiking domain to enhance the overall energy efficiency of the model. By capitalizing on the highly expressive topology emerging from the latent space of models trained with FFA, we develop a novel FF-SCP algorithm for OoD Detection. Our approach measures the likelihood of a sample belonging to the in-distribution (ID) data by using the distance from the latent representation of samples to class-representative manifolds. Additionally, to provide deeper insights into our OoD pipeline, we propose a gradient-free attribution technique that highlights the features of a sample pushing it away from the distribution of any class. Multiple experiments using our spiking FFA adaptation demonstrate that the achieved accuracy levels are comparable to those seen in analog networks trained via back-propagation. Furthermore, OoD detection experiments on multiple datasets prove that FF-SCP outperforms avant-garde OoD detectors within the spiking domain in terms of several metrics used in this area. We also present a qualitative analysis of our explainability technique, exposing the precision by which the method detects OoD features, such as embedded artifacts or missing regions.
User-Creator Feature Polarization in Recommender Systems with Dual Influence
Authors: Tao Lin, Kun Jin, Andrew Estornell, Xiaoying Zhang, YiLing Chen, Yang Liu
Date: 2024-07-19 00:00:00
Tasks: Diversity, Recommendation Systems

Recommender systems serve the dual purpose of presenting relevant content to users and helping content creators reach their target audience. The dual nature of these systems naturally influences both users and creators: users' preferences are affected by the items they are recommended, while creators may be incentivized to alter their content to attract more users. We define a model, called user-creator feature dynamics, to capture the dual influence of recommender systems. We prove that a recommender system with dual influence is guaranteed to polarize, causing diversity loss in the system. We then investigate, both theoretically and empirically, approaches for mitigating polarization and promoting diversity in recommender systems. Unexpectedly, we find that common diversity-promoting approaches do not work in the presence of dual influence, while relevancy-optimizing methods like top-$k$ truncation can prevent polarization and improve diversity of the system.
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
Authors: Yifan Wang, Xiao Luo, Chong Chen, Xian-Sheng Hua, Ming Zhang, Wei Ju
Date: 2024-07-19 00:00:00
Tasks: Classification, Graph Classification, Representation Learning

Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in real-world scenarios, labeled graph data can be limited or scarce. To address this issue, we focus on the problem of semi-supervised graph classification, which involves both supervised and unsupervised models learning from labeled and unlabeled data. In contrast to recent approaches that transfer the entire knowledge from the unsupervised model to the supervised one, we argue that an effective transfer should only retain the relevant semantics that align well with the supervised task. In this paper, we propose a novel framework named DisenSemi, which learns disentangled representation for semi-supervised graph classification. Specifically, a disentangled graph encoder is proposed to generate factor-wise graph representations for both supervised and unsupervised models. Then we train two models via supervised objective and mutual information (MI)-based constraints respectively. To ensure the meaningful transfer of knowledge from the unsupervised encoder to the supervised one, we further define an MI-based disentangled consistency regularization between two models and identify the corresponding rationale that aligns well with the current graph classification task. Experimental results on a range of publicly accessible datasets reveal the effectiveness of our DisenSemi.
Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field
Authors: Tobias Kerner
Date: 2024-07-19 00:00:00

There are many cases where LLMs are used for specific tasks in a single domain. These usually require less general, but more domain-specific knowledge. Highly capable, general-purpose state-of-the-art language models like GPT-4 or Claude-3-opus can often be used for such tasks, but they are very large and cannot be run locally, even if they were not proprietary. This can be a problem when working with sensitive data. This paper focuses on domain-specific and mixed-domain pretraining as potentially more efficient methods than general pretraining for specialized language models. We will take a look at work related to domain-specific pretraining, specifically in the medical area, and compare benchmark results of specialized language models to general-purpose language models.
MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction
Authors: Shuang Li, Ziyuan Pu, Nan Zhang, Duxin Chen, Lu Dong, Daniel J. Graham, Yinhai Wang
Date: 2024-07-19 00:00:00
Tasks: counterfactual, Prediction, Synthetic Data Generation, Traffic Prediction

Traffic crashes profoundly impede traffic efficiency and pose economic challenges. Accurate prediction of post-crash traffic status provides essential information for evaluating traffic perturbations and developing effective solutions. Previous studies have established a series of deep learning models to predict post-crash traffic conditions, however, these correlation-based methods cannot accommodate the biases caused by time-varying confounders and the heterogeneous effects of crashes. The post-crash traffic prediction model needs to estimate the counterfactual traffic speed response to hypothetical crashes under various conditions, which demonstrates the necessity of understanding the causal relationship between traffic factors. Therefore, this paper presents the Marginal Structural Causal Transformer (MSCT), a novel deep learning model designed for counterfactual post-crash traffic prediction. To address the issue of time-varying confounding bias, MSCT incorporates a structure inspired by Marginal Structural Models and introduces a balanced loss function to facilitate learning of invariant causal features. The proposed model is treatment-aware, with a specific focus on comprehending and predicting traffic speed under hypothetical crash intervention strategies. In the absence of ground-truth data, a synthetic data generation procedure is proposed to emulate the causal mechanism between traffic speed, crashes, and covariates. The model is validated using both synthetic and real-world data, demonstrating that MSCT outperforms state-of-the-art models in multi-step-ahead prediction performance. This study also systematically analyzes the impact of time-varying confounding bias and dataset distribution on model performance, contributing valuable insights into counterfactual prediction for intelligent transportation systems.
Towards the Causal Complete Cause of Multi-Modal Representation Learning
Authors: Jingyao Wang, Siyu Zhao, Wenwen Qiang, Jiangmeng Li, Changwen Zheng, Fuchun Sun, Hui Xiong
Date: 2024-07-19 00:00:00
Tasks: counterfactual, Representation Learning, Specificity

Multi-Modal Learning (MML) aims to learn effective representations across modalities for accurate predictions. Existing methods typically focus on modality consistency and specificity to learn effective representations. However, from a causal perspective, they may lead to representations that contain insufficient and unnecessary information. To address this, we propose that effective MML representations should be causally sufficient and necessary. Considering practical issues like spurious correlations and modality conflicts, we relax the exogeneity and monotonicity assumptions prevalent in prior works and explore the concepts specific to MML, i.e., Causal Complete Cause $C^3$. We begin by defining $C^3$, which quantifies the probability of representations being causally sufficient and necessary. We then discuss the identifiability of $C^3$ and introduce an instrumental variable to support identifying $C^3$ with non-exogeneity and non-monotonicity. Building on this, we conduct the $C^3$ measurement, i.e., \(C^3\) risk. We propose a twin network to estimate it through (i) the real-world branch: utilizing the instrumental variable for sufficiency, and (ii) the hypothetical-world branch: applying gradient-based counterfactual modeling for necessity. Theoretical analyses confirm its reliability. Based on these results, we propose $C^3$ Regularization, a plug-and-play method that enforces the causal completeness of the learned representations by minimizing $C^3$ risk. Extensive experiments demonstrate its effectiveness.
LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference
Authors: Qichen Fu, Minsik Cho, Thomas Merth, Sachin Mehta, Mohammad Rastegari, Mahyar Najibi
Date: 2024-07-19 00:00:00
Tasks: Question Answering

The inference of transformer-based large language models consists of two sequential stages: 1) a prefilling stage to compute the KV cache of prompts and generate the first token, and 2) a decoding stage to generate subsequent tokens. For long prompts, the KV cache must be computed for all tokens during the prefilling stage, which can significantly increase the time needed to generate the first token. Consequently, the prefilling stage may become a bottleneck in the generation process. An open question remains whether all prompt tokens are essential for generating the first token. To answer this, we introduce a novel method, LazyLLM, that selectively computes the KV for tokens important for the next token prediction in both the prefilling and decoding stages. Contrary to static pruning approaches that prune the prompt at once, LazyLLM allows language models to dynamically select different subsets of tokens from the context in different generation steps, even though they might be pruned in previous steps. Extensive experiments on standard datasets across various tasks demonstrate that LazyLLM is a generic method that can be seamlessly integrated with existing language models to significantly accelerate the generation without fine-tuning. For instance, in the multi-document question-answering task, LazyLLM accelerates the prefilling stage of the LLama 2 7B model by 2.34x while maintaining accuracy.
Rasa: Building Expressive Speech Synthesis Systems for Indian Languages in Low-resource Settings
Authors: Praveen Srinivasa Varadhan, Ashwin Sankar, Giri Raju, Mitesh M. Khapra
Date: 2024-07-19 00:00:00
Tasks: Expressive Speech Synthesis, Speech Synthesis

We release Rasa, the first multilingual expressive TTS dataset for any Indian language, which contains 10 hours of neutral speech and 1-3 hours of expressive speech for each of the 6 Ekman emotions covering 3 languages: Assamese, Bengali, & Tamil. Our ablation studies reveal that just 1 hour of neutral and 30 minutes of expressive data can yield a Fair system as indicated by MUSHRA scores. Increasing neutral data to 10 hours, with minimal expressive data, significantly enhances expressiveness. This offers a practical recipe for resource-constrained languages, prioritizing easily obtainable neutral data alongside smaller amounts of expressive data. We show the importance of syllabically balanced data and pooling emotions to enhance expressiveness. We also highlight challenges in generating specific emotions, e.g., fear and surprise.
Generative Language Model for Catalyst Discovery
Authors: Dong Hyeon Mok, Seoin Back
Date: 2024-07-19 00:00:00
Tasks: Language Modeling, Language Modelling, model, valid

Discovery of novel and promising materials is a critical challenge in the field of chemistry and material science, traditionally approached through methodologies ranging from trial-and-error to machine learning-driven inverse design. Recent studies suggest that transformer-based language models can be utilized as material generative models to expand chemical space and explore materials with desired properties. In this work, we introduce the Catalyst Generative Pretrained Transformer (CatGPT), trained to generate string representations of inorganic catalyst structures from a vast chemical space. CatGPT not only demonstrates high performance in generating valid and accurate catalyst structures but also serves as a foundation model for generating desired types of catalysts by fine-tuning with sparse and specified datasets. As an example, we fine-tuned the pretrained CatGPT using a binary alloy catalyst dataset designed for screening two-electron oxygen reduction reaction (2e-ORR) catalyst and generate catalyst structures specialized for 2e-ORR. Our work demonstrates the potential of language models as generative tools for catalyst discovery.
BERTer: The Efficient One
Authors: Pradyumna Saligram, Andrew Lanpouthakoun
Date: 2024-07-19 00:00:00
Tasks: Semantic Textual Similarity, Sentence, Sentence Embeddings, Sentiment Analysis

We explore advanced fine-tuning techniques to boost BERT's performance in sentiment analysis, paraphrase detection, and semantic textual similarity. Our approach leverages SMART regularization to combat overfitting, improves hyperparameter choices, employs a cross-embedding Siamese architecture for improved sentence embeddings, and introduces innovative early exiting methods. Our fine-tuning findings currently reveal substantial improvements in model efficiency and effectiveness when combining multiple fine-tuning architectures, achieving a state-of-the-art performance score of on the test set, surpassing current benchmarks and highlighting BERT's adaptability in multifaceted linguistic tasks.
Causal Inference with Complex Treatments: A Survey
Authors: Yingrong Wang, Haoxuan Li, Minqin Zhu, Anpeng Wu, Ruoxuan Xiong, Fei Wu, Kun Kuang
Date: 2024-07-19 00:00:00
Tasks: Causal Inference, Decision Making, Marketing, Survey

Causal inference plays an important role in explanatory analysis and decision making across various fields like statistics, marketing, health care, and education. Its main task is to estimate treatment effects and make intervention policies. Traditionally, most of the previous works typically focus on the binary treatment setting that there is only one treatment for a unit to adopt or not. However, in practice, the treatment can be much more complex, encompassing multi-valued, continuous, or bundle options. In this paper, we refer to these as complex treatments and systematically and comprehensively review the causal inference methods for addressing them. First, we formally revisit the problem definition, the basic assumptions, and their possible variations under specific conditions. Second, we sequentially review the related methods for multi-valued, continuous, and bundled treatment settings. In each situation, we tentatively divide the methods into two categories: those conforming to the unconfoundedness assumption and those violating it. Subsequently, we discuss the available datasets and open-source codes. Finally, we provide a brief summary of these works and suggest potential directions for future research.
NeuroBind: Towards Unified Multimodal Representations for Neural Signals
Authors: Fengyu Yang, Chao Feng, Daniel Wang, Tianye Wang, Ziyao Zeng, Zhiyang Xu, Hyoungseob Park, Pengliang Ji, Hanbin Zhao, Yuanning Li, Alex Wong
Date: 2024-07-19 00:00:00
Tasks: EEG

Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like electrophysiology and neuroimaging, reflects various aspects of information processing. Recent advances in deep neural networks offer new approaches to analyzing these signals using pre-trained models. However, challenges arise due to discrepancies between different neural signal modalities and the limited scale of high-quality neural data. To address these challenges, we present NeuroBind, a general representation that unifies multiple brain signal types, including EEG, fMRI, calcium imaging, and spiking data. To achieve this, we align neural signals in these image-paired neural datasets to pre-trained vision-language embeddings. Neurobind is the first model that studies different neural modalities interconnectedly and is able to leverage high-resource modality models for various neuroscience tasks. We also showed that by combining information from different neural signal modalities, NeuroBind enhances downstream performance, demonstrating the effectiveness of the complementary strengths of different neural modalities. As a result, we can leverage multiple types of neural signals mapped to the same space to improve downstream tasks, and demonstrate the complementary strengths of different neural modalities. This approach holds significant potential for advancing neuroscience research, improving AI systems, and developing neuroprosthetics and brain-computer interfaces.
Investigating the Indirect Object Identification circuit in Mamba
Authors: Danielle Ensign, Adrià Garriga-Alonso
Date: 2024-07-19 00:00:00
Tasks: Mamba, Object, Position

How well will current interpretability techniques generalize to future models? A relevant case study is Mamba, a recent recurrent architecture with scaling comparable to Transformers. We adapt pre-Mamba techniques to Mamba and partially reverse-engineer the circuit responsible for the Indirect Object Identification (IOI) task. Our techniques provide evidence that 1) Layer 39 is a key bottleneck, 2) Convolutions in layer 39 shift names one position forward, and 3) The name entities are stored linearly in Layer 39's SSM. Finally, we adapt an automatic circuit discovery tool, positional Edge Attribution Patching, to identify a Mamba IOI circuit. Our contributions provide initial evidence that circuit-based mechanistic interpretability tools work well for the Mamba architecture.
Time Series Generative Learning with Application to Brain Imaging Analysis
Authors: Zhenghao Li, Sanyou Wu, Long Feng
Date: 2024-07-19 00:00:00
Tasks: Alzheimer's Disease Detection, Data Augmentation, Image Generation, Time Series

This paper focuses on the analysis of sequential image data, particularly brain imaging data such as MRI, fMRI, CT, with the motivation of understanding the brain aging process and neurodegenerative diseases. To achieve this goal, we investigate image generation in a time series context. Specifically, we formulate a min-max problem derived from the $f$-divergence between neighboring pairs to learn a time series generator in a nonparametric manner. The generator enables us to generate future images by transforming prior lag-k observations and a random vector from a reference distribution. With a deep neural network learned generator, we prove that the joint distribution of the generated sequence converges to the latent truth under a Markov and a conditional invariance condition. Furthermore, we extend our generation mechanism to a panel data scenario to accommodate multiple samples. The effectiveness of our mechanism is evaluated by generating real brain MRI sequences from the Alzheimer's Disease Neuroimaging Initiative. These generated image sequences can be used as data augmentation to enhance the performance of further downstream tasks, such as Alzheimer's disease detection.
Clinical Reading Comprehension with Encoder-Decoder Models Enhanced by Direct Preference Optimization
Authors: Md Sultan Al Nahian, Ramakanth Kavuluru
Date: 2024-07-19 00:00:00
Tasks: Decoder, Extractive Question-Answering, Question Answering, Reading Comprehension

Extractive question answering over clinical text is a crucial need to help deal with the deluge of clinical text generated in hospitals. While encoder models (e.g., BERT) have been popular for this reading comprehension task, recently encoder-decoder models (e.g., T5) are on the rise. There is also the emergence of preference optimization techniques to align decoder-only LLMs with human preferences. In this paper, we combine encoder-decoder models with the direct preference optimization (DPO) method to improve over prior state of the art for the RadQA radiology question answering task by 12-15 F1 points. To the best of our knowledge, this effort is the first to show that DPO method also works for reading comprehension via novel heuristics to generate preference data without human inputs.
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models
Authors: Quan Li, Tianxiang Zhao, Lingwei Chen, Junjie Xu, Suhang Wang
Date: 2024-07-19 00:00:00
Tasks: Active Learning, Knowledge Graphs, Meta-Learning, Node Classification, Recommendation Systems, Transfer Learning

Graphs are pervasive in the real-world, such as social network analysis, bioinformatics, and knowledge graphs. Graph neural networks (GNNs) have great ability in node classification, a fundamental task on graphs. Unfortunately, conventional GNNs still face challenges in scenarios with few labeled nodes, despite the prevalence of few-shot node classification tasks in real-world applications. To address this challenge, various approaches have been proposed, including graph meta-learning, transfer learning, and methods based on Large Language Models (LLMs). However, traditional meta-learning and transfer learning methods often require prior knowledge from base classes or fail to exploit the potential advantages of unlabeled nodes. Meanwhile, LLM-based methods may overlook the zero-shot capabilities of LLMs and rely heavily on the quality of generated contexts. In this paper, we propose a novel approach that integrates LLMs and GNNs, leveraging the zero-shot inference and reasoning capabilities of LLMs and employing a Graph-LLM-based active learning paradigm to enhance GNNs' performance. Extensive experiments demonstrate the effectiveness of our model in improving node classification accuracy with considerably limited labeled data, surpassing state-of-the-art baselines by significant margins.