OpenRaiser/Intern-Atlas huggingface dataset:
Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach
Venue: arXiv cs.LG 2023
Year: 2023

Invariant representation learning (IRL) encourages the prediction from invariant causal features to labels de-confounded from the environments, advancing the technical roadmap of out-of-distribution (OOD) generalization. Despite spotlights around, recent theoretical results verified that some causal features recovered by IRLs merely pretend domain-invariantly in the training environments but fail in unseen domains. The \emph{fake invariance} severely endangers OOD generalization since the trustful objective can not be diagnosed and existing causal surgeries are invalid to rectify. In this paper, we review a IRL family (InvRat) under the Partially and Fully Informative Invariant Feature Structural Causal Models (PIIF SCM /FIIF SCM) respectively, to certify their weaknesses in representing fake invariant features, then, unify their causal diagrams to propose ReStructured SCM (RS-SCM). RS-SCM can ideally rebuild the spurious and the fake invariant features simultaneously. Given this, we further develop an approach based on conditional mutual information with respect to RS-SCM, then rigorously rectify the spurious and fake invariant effects. It can be easily implemented by a small feature selection subnet introduced in the IRL family, which is alternatively optimized to achieve our goal. Experiments verified the superiority of our approach to fight against the fake invariant issue across a variety of OOD generalization benchmarks.
Beyond Over-Protection: A Targeted Approach to Spectre Mitigation and Performance Optimization
Venue: arXiv cs.CR 2023
Year: 2023

Since the advent of Spectre attacks, researchers and practitioners have developed a range of hardware and software measures to counter transient execution attacks. A prime example of such mitigation is speculative load hardening in LLVM, which protects against leaks by tracking the speculation state and masking values during misspeculation. LLVM relies on static analysis to harden programs using slh that often results in over-protection, which incurs performance overhead. We extended an existing side-channel model validation framework, Scam-V, to check the vulnerability of programs to Spectre-PHT attacks and optimize the protection of programs using the slh approach. We illustrate the efficacy of Scam-V by first demonstrating that it can automatically identify Spectre vulnerabilities in real programs, e.g., fragments of crypto-libraries. We then develop an optimization mechanism that validates the necessity of slh hardening w.r.t. the target platform. Our experiments showed that hardening introduced by LLVM in most cases could be significantly improved when the underlying microarchitecture properties are considered.
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
Venue: arXiv cs.LG 2023
Year: 2023

Explaining predictions of black-box neural networks is crucial when applied to decision-critical tasks. Thus, attribution maps are commonly used to identify important image regions, despite prior work showing that humans prefer explanations based on similar examples. To this end, ProtoPNet learns a set of class-representative feature vectors (prototypes) for case-based reasoning. During inference, similarities of latent features to prototypes are linearly classified to form predictions and attribution maps are provided to explain the similarity. In this work, we evaluate whether architectures for case-based reasoning fulfill established axioms required for faithful explanations using the example of ProtoPNet. We show that such architectures allow the extraction of faithful explanations. However, we prove that the attribution maps used to explain the similarities violate the axioms. We propose a new procedure to extract explanations for trained ProtoPNets, named ProtoPFaith. Conceptually, these explanations are Shapley values, calculated on the similarity scores of each prototype. They allow to faithfully answer which prototypes are present in an unseen image and quantify each pixel's contribution to that presence, thereby complying with all axioms. The theoretical violations of ProtoPNet manifest in our experiments on three datasets (CUB-200-2011, Stanford Dogs, RSNA) and five architectures (ConvNet, ResNet, ResNet50, WideResNet50, ResNeXt50). Our experiments show a qualitative difference between the explanations given by ProtoPNet and ProtoPFaith. Additionally, we quantify the explanations with the Area Over the Perturbation Curve, on which ProtoPFaith outperforms ProtoPNet on all experiments by a factor $>10^3$.
End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation
Venue: arXiv cs.LG 2023
Year: 2023

In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous work, we train NNs directly on object detection maps. We do so by performing a continuous relaxation of the cell-averaging constant false alarm rate (CA-CFAR) peak detector, which is a well-established algorithm for object detection using radar. With this new training objective we are able to increase object detection performance by a large margin. Furthermore, we introduce separable convolution kernels to strongly reduce the number of parameters and computational complexity of convolutional NN architectures for radar applications. We validate our contributions with experiments on real-world measurement data and compare them against signal processing interference mitigation methods.
Disentangling Linear Mode-Connectivity
Venue: arXiv cs.LG 2023
Year: 2023

Linear mode-connectivity (LMC) (or lack thereof) is one of the intriguing characteristics of neural network loss landscapes. While empirically well established, it unfortunately still lacks a proper theoretical understanding. Even worse, although empirical data points are abound, a systematic study of when networks exhibit LMC is largely missing in the literature. In this work we aim to close this gap. We explore how LMC is affected by three factors: (1) architecture (sparsity, weight-sharing), (2) training strategy (optimization setup) as well as (3) the underlying dataset. We place particular emphasis on minimal but non-trivial settings, removing as much unnecessary complexity as possible. We believe that our insights can guide future theoretical works on uncovering the inner workings of LMC.
Learning in Online Principal-Agent Interactions: The Power of Menus
Venue: arXiv cs.GT 2023
Year: 2023

We study a ubiquitous learning challenge in online principal-agent problems during which the principal learns the agent's private information from the agent's revealed preferences in historical interactions. This paradigm includes important special cases such as pricing and contract design, which have been widely studied in recent literature. However, existing work considers the case where the principal can only choose a single strategy at every round to interact with the agent and then observe the agent's revealed preference through their actions. In this paper, we extend this line of study to allow the principal to offer a menu of strategies to the agent and learn additionally from observing the agent's selection from the menu. We provide a thorough investigation of several online principal-agent problem settings and characterize their sample complexities, accompanied by the corresponding algorithms we have developed. We instantiate this paradigm to several important design problems $-$ including Stackelberg (security) games, contract design, and information design. Finally, we also explore the connection between our findings and existing results about online learning in Stackelberg games, and we offer a solution that can overcome a key hard instance of Peng et al. (2019).
Reliable Probabilistic Classification with Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Venn Prediction (VP) is a new machine learning framework for producing well-calibrated probabilistic predictions. In particular it provides well-calibrated lower and upper bounds for the conditional probability of an example belonging to each possible class of the problem at hand. This paper proposes five VP methods based on Neural Networks (NNs), which is one of the most widely used machine learning techniques. The proposed methods are evaluated experimentally on four benchmark datasets and the obtained results demonstrate the empirical well-calibratedness of their outputs and their superiority over the outputs of the traditional NN classifier.
DHFormer: A Vision Transformer-Based Attention Module for Image Dehazing
Venue: arXiv cs.CV 2023
Year: 2023

Images acquired in hazy conditions have degradations induced in them. Dehazing such images is a vexed and ill-posed problem. Scores of prior-based and learning-based approaches have been proposed to mitigate the effect of haze and generate haze-free images. Many conventional methods are constrained by their lack of awareness regarding scene depth and their incapacity to capture long-range dependencies. In this paper, a method that uses residual learning and vision transformers in an attention module is proposed. It essentially comprises two networks: In the first one, the network takes the ratio of a hazy image and the approximated transmission matrix to estimate a residual map. The second network takes this residual image as input and passes it through convolution layers before superposing it on the generated feature maps. It is then passed through global context and depth-aware transformer encoders to obtain channel attention. The attention module then infers the spatial attention map before generating the final haze-free image. Experimental results, including several quantitative metrics, demonstrate the efficiency and scalability of the suggested methodology.
Distilling Large Language Models for Matching Patients to Clinical Trials
Venue: arXiv cs.AI 2023
Year: 2023

The recent success of large language models (LLMs) has paved the way for their adoption in the high-stakes domain of healthcare. Specifically, the application of LLMs in patient-trial matching, which involves assessing patient eligibility against clinical trial's nuanced inclusion and exclusion criteria, has shown promise. Recent research has shown that GPT-3.5, a widely recognized LLM developed by OpenAI, can outperform existing methods with minimal 'variable engineering' by simply comparing clinical trial information against patient summaries. However, there are significant challenges associated with using closed-source proprietary LLMs like GPT-3.5 in practical healthcare applications, such as cost, privacy and reproducibility concerns. To address these issues, this study presents the first systematic examination of the efficacy of both proprietary (GPT-3.5, and GPT-4) and open-source LLMs (LLAMA 7B,13B, and 70B) for the task of patient-trial matching. Employing a multifaceted evaluation framework, we conducted extensive automated and human-centric assessments coupled with a detailed error analysis for each model. To enhance the adaptability of open-source LLMs, we have created a specialized synthetic dataset utilizing GPT-4, enabling effective fine-tuning under constrained data conditions. Our findings reveal that open-source LLMs, when fine-tuned on this limited and synthetic dataset, demonstrate performance parity with their proprietary counterparts. This presents a massive opportunity for their deployment in real-world healthcare applications. To foster further research and applications in this field, we release both the annotated evaluation dataset along with the fine-tuned LLM -- Trial-LLAMA -- for public use.
GreenLightningAI: An Efficient AI System with Decoupled Structural and Quantitative Knowledge
Venue: arXiv cs.LG 2023
Year: 2023

The number and complexity of artificial intelligence (AI) applications is growing relentlessly. As a result, even with the many algorithmic and mathematical advances experienced over past decades as well as the impressive energy efficiency and computational capacity of current hardware accelerators, training the most powerful and popular deep neural networks comes at very high economic and environmental costs. Recognising that additional optimisations of conventional neural network training is very difficult, this work takes a radically different approach by proposing GreenLightningAI, a new AI system design consisting of a linear model that is capable of emulating the behaviour of deep neural networks by subsetting the model for each particular sample. The new AI system stores the information required to select the system subset for a given sample (referred to as structural information) separately from the linear model parameters (referred to as quantitative knowledge). In this paper we present a proof of concept, showing that the structural information stabilises far earlier than the quantitative knowledge. Additionally, we show experimentally that the structural information can be kept unmodified when re-training the AI system with new samples while still achieving a validation accuracy similar to that obtained when re-training a neural network with similar size. Since the proposed AI system is based on a linear model, multiple copies of the model, trained with different datasets, can be easily combined. This enables faster and greener (re)-training algorithms, including incremental re-training and federated incremental re-training.
Faithful Persona-based Conversational Dataset Generation with Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

High-quality conversational datasets are essential for developing AI models that can communicate with users. One way to foster deeper interactions between a chatbot and its user is through personas, aspects of the user's character that provide insights into their personality, motivations, and behaviors. Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations. The Generator is an LLM prompted to output conversations. The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations. These experts select the best generated conversations, which we then use to improve the Generator. We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat. We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during Turing test decreases from 17.2% to 8.8% over three iterations.
Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
Venue: arXiv cs.IR 2023
Year: 2023

In production applications of recommender systems, a continuous data flow is employed to update models in real-time. Many recommender models often require complete retraining to adapt to new data. In this work, we introduce a novel collaborative filtering model for sequential problems known as Tucker Integrator Recommender - TIRecA. TIRecA efficiently updates its parameters using only the new data segment, allowing incremental addition of new users and items to the recommender system. To demonstrate the effectiveness of the proposed model, we conducted experiments on four publicly available datasets: MovieLens 20M, Amazon Beauty, Amazon Toys and Games, and Steam. Our comparison with general matrix and tensor-based baselines in terms of prediction quality and computational time reveals that TIRecA achieves comparable quality to the baseline methods, while being 10-20 times faster in training time.
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Venue: arXiv cs.CY 2023
Year: 2023

Text-to-image diffusion models have been adopted into key commercial workflows, such as art generation and image editing. Characterising the implicit social biases they exhibit, such as gender and racial stereotypes, is a necessary first step in avoiding discriminatory outcomes. While existing studies on social bias focus on image generation, the biases exhibited in alternate applications of diffusion-based foundation models remain under-explored. We propose methods that use synthetic images to probe two applications of diffusion models, image editing and classification, for social bias. Using our methodology, we uncover meaningful and significant inter-sectional social biases in \textit{Stable Diffusion}, a state-of-the-art open-source text-to-image model. Our findings caution against the uninformed adoption of text-to-image foundation models for downstream tasks and services.
STAGER checklist: Standardized Testing and Assessment Guidelines for Evaluating Generative AI Reliability
Venue: arXiv cs.HC 2023
Year: 2023

Generative Artificial Intelligence (AI) holds immense potential in medical applications. Numerous studies have explored the efficacy of various generative AI models within healthcare contexts, but there is a lack of a comprehensive and systematic evaluation framework. Given that some studies evaluating the ability of generative AI for medical applications have deficiencies in their methodological design, standardized guidelines for their evaluation are also currently lacking. In response, our objective is to devise standardized assessment guidelines tailored for evaluating the performance of generative AI systems in medical contexts. To this end, we conducted a thorough literature review using the PubMed and Google Scholar databases, focusing on research that tests generative AI capabilities in medicine. Our multidisciplinary team, comprising experts in life sciences, clinical medicine, medical engineering, and generative AI users, conducted several discussion sessions and developed a checklist of 23 items. The checklist is designed to encompass the critical evaluation aspects of generative AI in medical applications comprehensively. This checklist, and the broader assessment framework it anchors, address several key dimensions, including question collection, querying methodologies, and assessment techniques. We aim to provide a holistic evaluation of AI systems. The checklist delineates a clear pathway from question gathering to result assessment, offering researchers guidance through potential challenges and pitfalls. Our framework furnishes a standardized, systematic approach for research involving the testing of generative AI's applicability in medicine. It enhances the quality of research reporting and aids in the evolution of generative AI in medicine and life sciences.
Verbesserung des Record Linkage für die Gesundheitsforschung in Deutschland
Venue: arXiv cs.CY 2023
Year: 2023

Record linkage means linking data from multiple sources. This approach enables the answering of scientific questions that cannot be addressed using single data sources due to limited variables. The potential of linked data for health research is enormous, as it can enhance prevention, treatment, and population health policies. Due the sensitivity of health data, there are strict legal requirements to prevent potential misuse. However, these requirements also limit the use of health data for research, thereby hindering innovations in prevention and care. Also, comprehensive Record linkage in Germany is often challenging due to lacking unique personal identifiers or interoperable solutions. Rather, the need to protect data is often weighed against the importance of research aiming at healthcare enhancements: for instance, data protection officers may demand the informed consent of individual study participants for data linkage, even when this is not mandatory. Furthermore, legal frameworks may be interpreted differently on varying occasions. Given both, technical and legal challenges, record linkage for health research in Germany falls behind the standards of other European countries. To ensure successful record linkage, case-specific solutions must be developed, tested, and modified as necessary before implementation. This paper discusses limitations and possibilities of various data linkage approaches tailored to different use cases in compliance with the European General Data Protection Regulation. It further describes requirements for achieving a more research-friendly approach to linking health data records in Germany. Additionally, it provides recommendations to legislators. The objective of this work is to improve record linkage for health research in Germany.
Casual Social Media Use among the Youth: Effects on Online and Offline Political Participation
Venue: arXiv cs.CY 2023
Year: 2023

Background: Previous studies suggest that social media use among the youth is correlated with online and offline political participation. There is also a mixed and inconclusive debate on whether more online political participation in the youth increases their offline political participation. Methods: This study uses three models of OLS, two-way fixed effects, and an instrumental variable approach to make causal inferences about social media use, online, and offline political participation of the youth. Findings: The analyses provide evidence of a large effect of casual social media use on online political participation, and no effect or negligible effect on offline political participation and voting behavior. The results from fixed effects and instrumental variable models provide strong evidence of elasticity between online and offline political participation in young individuals. On average, a one percent increase in online political participation increases the offline political activity index by 0.12 percent.
MVHuman: Tailoring 2D Diffusion with Multi-view Sampling For Realistic 3D Human Generation
Venue: arXiv cs.CV 2023
Year: 2023

Recent months have witnessed rapid progress in 3D generation based on diffusion models. Most advances require fine-tuning existing 2D Stable Diffsuions into multi-view settings or tedious distilling operations and hence fall short of 3D human generation due to the lack of diverse 3D human datasets. We present an alternative scheme named MVHuman to generate human radiance fields from text guidance, with consistent multi-view images directly sampled from pre-trained Stable Diffsuions without any fine-tuning or distilling. Our core is a multi-view sampling strategy to tailor the denoising processes of the pre-trained network for generating consistent multi-view images. It encompasses view-consistent conditioning, replacing the original noises with ``consistency-guided noises'', optimizing latent codes, as well as utilizing cross-view attention layers. With the multi-view images through the sampling process, we adopt geometry refinement and 3D radiance field generation followed by a subsequent neural blending scheme for free-view rendering. Extensive experiments demonstrate the efficacy of our method, as well as its superiority to state-of-the-art 3D human generation methods.