OpenRaiser/Intern-Atlas huggingface dataset:
Relevance-aware Algorithmic Recourse
Venue: arXiv cs.LG 2024
Year: 2024

As machine learning continues to gain prominence, transparency and explainability are increasingly critical. Without an understanding of these models, they can replicate and worsen human bias, adversely affecting marginalized communities. Algorithmic recourse emerges as a tool for clarifying decisions made by predictive models, providing actionable insights to alter outcomes. They answer, 'What do I have to change?' to achieve the desired result. Despite their importance, current algorithmic recourse methods treat all domain values equally, which is unrealistic in real-world settings. In this paper, we propose a novel framework, Relevance-Aware Algorithmic Recourse (RAAR), that leverages the concept of relevance in applying algorithmic recourse to regression tasks. We conducted multiple experiments on 15 datasets to outline how relevance influences recourses. Results show that relevance contributes algorithmic recourses comparable to well-known baselines, with greater efficiency and lower relative costs.
Patch-enhanced Mask Encoder Prompt Image Generation
Venue: arXiv cs.AI 2024
Year: 2024

Artificial Intelligence Generated Content(AIGC), known for its superior visual results, represents a promising mitigation method for high-cost advertising applications. Numerous approaches have been developed to manipulate generated content under different conditions. However, a crucial limitation lies in the accurate description of products in advertising applications. Applying previous methods directly may lead to considerable distortion and deformation of advertised products, primarily due to oversimplified content control conditions. Hence, in this work, we propose a patch-enhanced mask encoder approach to ensure accurate product descriptions while preserving diverse backgrounds. Our approach consists of three components Patch Flexible Visibility, Mask Encoder Prompt Adapter and an image Foundation Model. Patch Flexible Visibility is used for generating a more reasonable background image. Mask Encoder Prompt Adapter enables region-controlled fusion. We also conduct an analysis of the structure and operational mechanisms of the Generation Module. Experimental results show our method can achieve the highest visual results and FID scores compared with other methods.
Benchmarking and Improving Detail Image Caption
Venue: arXiv cs.CV 2024
Year: 2024

Image captioning has long been regarded as a fundamental task in visual understanding. Recently, however, few large vision-language model (LVLM) research discusses model's image captioning performance because of the outdated short-caption benchmarks and unreliable evaluation metrics. In this work, we propose to benchmark detail image caption task by curating high-quality evaluation datasets annotated by human experts, GPT-4V and Gemini-1.5-Pro. We also design a more reliable caption evaluation metric called CAPTURE (CAPtion evaluation by exTracting and coUpling coRE information). CAPTURE extracts visual elements, e.g., objects, attributes and relations from captions, and then matches these elements through three stages, achieving the highest consistency with expert judgements over other rule-based or model-based caption metrics. The proposed benchmark and metric provide reliable evaluation for LVLM's detailed image captioning ability. Guided by this evaluation, we further explore to unleash LVLM's detail caption capabilities by synthesizing high-quality data through a five-stage data construction pipeline. Our pipeline only uses a given LVLM itself and other open-source tools, without any human or GPT-4V annotation in the loop. Experiments show that the proposed data construction strategy significantly improves model-generated detail caption data quality for LVLMs with leading performance, and the data quality can be further improved in a self-looping paradigm. All code and dataset will be publicly available at https://github.com/foundation-multimodal-models/CAPTURE.
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Venue: arXiv cs.LG 2024
Year: 2024

The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is a quadruplet composed of a prompt, two independent responses (completions of the prompt) and a human preference between the two independent responses, yielding a preferred and a dis-preferred response. Such data is typically scarce and expensive to collect. On the other hand, \emph{single-trajectory} datasets where each element is a triplet composed of a prompt, a response and a human feedback is naturally more abundant. The canonical element of such datasets is for instance an LLM's response to a user's prompt followed by a user's feedback such as a thumbs-up/down. Consequently, in this work, we propose DRO, or \emph{Direct Reward Optimisation}, as a framework and associated algorithms that do not require pairwise preferences. DRO uses a simple mean-squared objective that can be implemented in various ways. We validate our findings empirically, using T5 encoder-decoder language models, and show DRO's performance over selected baselines such as Kahneman-Tversky Optimization (KTO). Thus, we confirm that DRO is a simple and empirically compelling method for single-trajectory policy optimisation.
DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems
Venue: arXiv cs.CE 2024
Year: 2024

The modern digital engineering design often requires costly repeated simulations for different scenarios. The prediction capability of neural networks (NNs) makes them suitable surrogates for providing design insights. However, only a few NNs can efficiently handle complex engineering scenario predictions. We introduce a new version of the neural operators called DeepOKAN, which utilizes Kolmogorov Arnold networks (KANs) rather than the conventional neural network architectures. Our DeepOKAN uses Gaussian radial basis functions (RBFs) rather than the B-splines. RBFs offer good approximation properties and are typically computationally fast. The KAN architecture, combined with RBFs, allows DeepOKANs to represent better intricate relationships between input parameters and output fields, resulting in more accurate predictions across various mechanics problems. Specifically, we evaluate DeepOKAN's performance on several mechanics problems, including 1D sinusoidal waves, 2D orthotropic elasticity, and transient Poisson's problem, consistently achieving lower training losses and more accurate predictions compared to traditional DeepONets. This approach should pave the way for further improving the performance of neural operators.
Dress Anyone : Automatic Physically-Based Garment Pattern Refitting
Venue: arXiv cs.GR 2024
Year: 2024

Well-fitted clothing is essential for both real and virtual garments to enable self-expression and accurate representation for a large variety of body types. Common practice in the industry is to provide a pre-made selection of distinct garment sizes such as small, medium and large. While these may cater to certain groups of individuals that fall within this distribution, they often exclude large sections of the population. In contrast, individually tailored clothing offers a solution to obtain custom-fit garments that are tailored to each individual. However, manual tailoring is time-consuming and requires specialized knowledge, prohibiting the approach from being applied to produce fitted clothing at scale. To address this challenge, we propose a novel method leveraging differentiable simulation for refitting and draping 3D garments and their corresponding 2D pattern panels onto a new body shape, enabling a workflow where garments only need to be designed once, in a single size, and they can be automatically refitted to support numerous body size and shape variations. Our method enables downstream applications, where our optimized 3D drape can be directly ingested into game engines or other applications. Our 2D sewing patterns allow for accurate physics-based simulations and enables manufacturing clothing for the real world.
The ethical situation of DALL-E 2
Venue: arXiv cs.CY 2024
Year: 2024

A hot topic of Artificial Intelligence right now is image generation from prompts. DALL-E 2 is one of the biggest names in this domain, as it allows people to create images from simple text inputs, to even more complicated ones. The company that made this possible, OpenAI, has assured everyone that visited their website that their mission is to ensure that artificial general intelligence benefits all humanity. A noble idea in our opinion, that also stood as the motive behind us choosing this subject. This paper analyzes the ethical implications of an AI image generative system, with an emphasis on how society is responding to it, how it probably will and how it should if all the right measures are taken.
$E^{3}$Gen: Efficient, Expressive and Editable Avatars Generation
Venue: arXiv cs.CV 2024
Year: 2024

This paper aims to introduce 3D Gaussian for efficient, expressive, and editable digital avatar generation. This task faces two major challenges: (1) The unstructured nature of 3D Gaussian makes it incompatible with current generation pipelines; (2) the expressive animation of 3D Gaussian in a generative setting that involves training with multiple subjects remains unexplored. In this paper, we propose a novel avatar generation method named $E^3$Gen, to effectively address these challenges. First, we propose a novel generative UV features plane representation that encodes unstructured 3D Gaussian onto a structured 2D UV space defined by the SMPL-X parametric model. This novel representation not only preserves the representation ability of the original 3D Gaussian but also introduces a shared structure among subjects to enable generative learning of the diffusion model. To tackle the second challenge, we propose a part-aware deformation module to achieve robust and accurate full-body expressive pose control. Extensive experiments demonstrate that our method achieves superior performance in avatar generation and enables expressive full-body pose control and editing. Our project page is https://olivia23333.github.io/E3Gen.
Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning
Venue: arXiv cs.LG 2024
Year: 2024

Machine learning models are vulnerable to adversarial attacks, including attacks that leak information about the model's training data. There has recently been an increase in interest about how to best address privacy concerns, especially in the presence of data-removal requests. Machine unlearning algorithms aim to efficiently update trained models to comply with data deletion requests while maintaining performance and without having to resort to retraining the model from scratch, a costly endeavor. Several algorithms in the machine unlearning literature demonstrate some level of privacy gains, but they are often evaluated only on rudimentary membership inference attacks, which do not represent realistic threats. In this paper we describe and propose alternative evaluation methods for three key shortcomings in the current evaluation of unlearning algorithms. We show the utility of our alternative evaluations via a series of experiments of state-of-the-art unlearning algorithms on different computer vision datasets, presenting a more detailed picture of the state of the field.
Synthetic Potential Outcomes and Causal Mixture Identifiability
Venue: arXiv cs.LG 2024
Year: 2024

Heterogeneous data from multiple populations, sub-groups, or sources is often represented as a ``mixture model'' with a single latent class influencing all of the observed covariates. Heterogeneity can be resolved at multiple levels by grouping populations according to different notions of similarity. This paper proposes grouping with respect to the causal response of an intervention or perturbation on the system. This definition is distinct from previous notions, such as similar covariate values (e.g. clustering) or similar correlations between covariates (e.g. Gaussian mixture models). To solve the problem, we ``synthetically sample'' from a counterfactual distribution using higher-order multi-linear moments of the observable data. To understand how these ``causal mixtures'' fit in with more classical notions, we develop a hierarchy of mixture identifiability.
Human-Aware Belief Revision: A Cognitively Inspired Framework for Explanation-Guided Revision of Human Models
Venue: arXiv cs.AI 2024
Year: 2024

Traditional belief revision frameworks often rely on the principle of minimalism, which advocates minimal changes to existing beliefs. However, research in human cognition suggests that people are inherently driven to seek explanations for inconsistencies, thereby striving for explanatory understanding rather than minimal changes when revising beliefs. Traditional frameworks often fail to account for these cognitive patterns, relying instead on formal principles that may not reflect actual human reasoning. To address this gap, we introduce Human-Aware Belief Revision, a cognitively-inspired framework for modeling human belief revision dynamics, where given a human model and an explanation for an explanandum, revises the model in a non-minimal way that aligns with human cognition. Finally, we conduct two human-subject studies to empirically evaluate our framework under real-world scenarios. Our findings support our hypotheses and provide insights into the strategies people employ when resolving inconsistencies, offering some guidance for developing more effective human-aware AI systems.
Faster Cascades via Speculative Decoding
Venue: arXiv cs.CL 2024
Year: 2024

Cascades and speculative decoding are two common approaches to improving language models' inference efficiency. Both approaches involve interleaving models of different sizes, but via fundamentally distinct mechanisms: cascades employ a deferral rule that invokes the larger model only for "hard" inputs, while speculative decoding uses speculative execution to primarily invoke the larger model in parallel verification mode. These mechanisms offer different benefits: empirically, cascades offer better cost-quality trade-offs, often even outperforming the large model, while theoretically, speculative decoding offers a guarantee of quality-neutrality. In this paper, we leverage the best of both these approaches by designing new speculative cascading techniques that implement their deferral rule through speculative execution. We characterize the optimal deferral rule for our speculative cascades, and employ a plug-in approximation to the optimal rule. Experiments with Gemma and T5 models on a range of language benchmarks show that our approach yields better cost quality trade-offs than cascading and speculative decoding baselines.
Safe and Efficient Estimation for Robotics through the Optimal Use of Resources
Venue: arXiv cs.RO 2024
Year: 2024

In order to operate in and interact with the physical world, robots need to have estimates of the current and future state of the environment. We thus equip robots with sensors and build models and algorithms that, given some measurements, produce estimates of the current or future states. Environments can be unpredictable and sensors are not perfect. Therefore, it is important to both use all information available, and to do so optimally: making sure that we get the best possible answer from the amount of information we have. However, in prevalent research, uncommon sensors, such as sound or radio-frequency signals, are commonly ignored for state estimation; and the most popular solvers employed to produce state estimates are only of local nature, meaning they may produce suboptimal estimates for the typically non-convex estimation problems. My research aims to use resources more optimally, by building on 1) multi-modality: using ubiquitous RF transceivers and microphones to support state estimation, 2) building certifiably optimal solvers and 3) learning and improving adequate models from data.
Real-Time Environment Condition Classification for Autonomous Vehicles
Venue: arXiv cs.CV 2024
Year: 2024

Current autonomous driving technologies are being rolled out in geo-fenced areas with well-defined operation conditions such as time of operation, area, weather conditions and road conditions. In this way, challenging conditions as adverse weather, slippery road or densely-populated city centers can be excluded. In order to lift the geo-fenced restriction and allow a more dynamic availability of autonomous driving functions, it is necessary for the vehicle to autonomously perform an environment condition assessment in real time to identify when the system cannot operate safely and either stop operation or require the resting passenger to take control. In particular, adverse-weather challenges are a fundamental limitation as sensor performance degenerates quickly, prohibiting the use of sensors such as cameras to locate and monitor road signs, pedestrians or other vehicles. To address this issue, we train a deep learning model to identify outdoor weather and dangerous road conditions, enabling a quick reaction to new situations and environments. We achieve this by introducing an improved taxonomy and label hierarchy for a state-of-the-art adverse-weather dataset, relabelling it with a novel semi-automated labeling pipeline. Using the novel proposed dataset and hierarchy, we train RECNet, a deep learning model for the classification of environment conditions from a single RGB frame. We outperform baseline models by relative 16% in F1- Score, while maintaining a real-time capable performance of 20 Hz.
Enhancing Trust and Security in the Vehicular Metaverse: A Reputation-Based Mechanism for Participants with Moral Hazard
Venue: arXiv cs.CR 2024
Year: 2024

In this paper, we tackle the issue of moral hazard within the realm of the vehicular Metaverse. A pivotal facilitator of the vehicular Metaverse is the effective orchestration of its market elements, primarily comprised of sensing internet of things (SIoT) devices. These SIoT devices play a critical role by furnishing the virtual service provider (VSP) with real-time sensing data, allowing for the faithful replication of the physical environment within the virtual realm. However, SIoT devices with intentional misbehavior can identify a loophole in the system post-payment and proceeds to deliver falsified content, which cause the whole vehicular Metaverse to collapse. To combat this significant problem, we propose an incentive mechanism centered around a reputation-based strategy. Specifically, the concept involves maintaining reputation scores for participants based on their interactions with the VSP. These scores are derived from feedback received by the VSP from Metaverse users regarding the content delivered by the VSP and are managed using a subjective logic model. Nevertheless, to prevent ``good" SIoT devices with false positive ratings to leave the Metaverse market, we build a vanishing-like system of previous ratings so that the VSP can make informed decisions based on the most recent and accurate data available. Finally, we validate our proposed model through extensive simulations. Our primary results show that our mechanism can efficiently prevent malicious devices from starting their poisoning attacks. At the same time, trustworthy SIoT devices that had a previous miss-classification are not banned from the market.
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning
Venue: arXiv cs.LG 2024
Year: 2024

Designing Reinforcement Learning (RL) solutions for real-life problems remains a significant challenge. A major area of concern is safety. "Shielding" is a popular technique to enforce safety in RL by turning user-defined safety specifications into safe agent behavior. However, these methods either suffer from extreme learning delays, demand extensive human effort in designing models and safe domains in the problem, or require pre-computation. In this paper, we propose a new permissibility-based framework to deal with safety and shield construction. Permissibility was originally designed for eliminating (non-permissible) actions that will not lead to an optimal solution to improve RL training efficiency. This paper shows that safety can be naturally incorporated into this framework, i.e. extending permissibility to include safety, and thereby we can achieve both safety and improved efficiency. Experimental evaluation using three standard RL applications shows the effectiveness of the approach.
Deep Learning for Assessment of Oral Reading Fluency
Venue: arXiv cs.CL 2024
Year: 2024

Reading fluency assessment is a critical component of literacy programmes, serving to guide and monitor early education interventions. Given the resource intensive nature of the exercise when conducted by teachers, the development of automatic tools that can operate on audio recordings of oral reading is attractive as an objective and highly scalable solution. Multiple complex aspects such as accuracy, rate and expressiveness underlie human judgements of reading fluency. In this work, we investigate end-to-end modeling on a training dataset of children's audio recordings of story texts labeled by human experts. The pre-trained wav2vec2.0 model is adopted due its potential to alleviate the challenges from the limited amount of labeled data. We report the performance of a number of system variations on the relevant measures, and also probe the learned embeddings for lexical and acoustic-prosodic features known to be important to the perception of reading fluency.
Conformal Recursive Feature Elimination
Venue: arXiv cs.CV 2024
Year: 2024

Unlike traditional statistical methods, Conformal Prediction (CP) allows for the determination of valid and accurate confidence levels associated with individual predictions based only on exchangeability of the data. We here introduce a new feature selection method that takes advantage of the CP framework. Our proposal, named Conformal Recursive Feature Elimination (CRFE), identifies and recursively removes features that increase the non-conformity of a dataset. We also present an automatic stopping criterion for CRFE, as well as a new index to measure consistency between subsets of features. CRFE selections are compared to the classical Recursive Feature Elimination (RFE) method on several multiclass datasets by using multiple partitions of the data. The results show that CRFE clearly outperforms RFE in half of the datasets, while achieving similar performance in the rest. The automatic stopping criterion provides subsets of effective and non-redundant features without computing any classification performance.