DNN的成功是由过度参数化网络概括的违反直觉能力驱动的，即使它们完全适合培训数据。实际上，测试误差通常会随着过度参数化的增加而继续减少，称为双重下降。这使从业者可以实例化大型模型，而不必担心过度合适。但是，尽管有好处，但先前的工作表明，过度参数会加剧偏见对少数族裔亚组。已经提出了几种公平约束的DNN培训方法来解决这一问题。在这里，我们对Mindiff进行了严格的研究，这是Tensorflow负责AI工具包中实施的公平约束培训程序，旨在实现机会平等。我们表明，尽管Mindiff改善了参数化不足的模型的公平性，但在过度参数化的制度中可能是无效的。这是因为一个具有零训练损失的过度合适模型在培训数据上是微不足道的，造成了“公平幻想”，因此可以关闭Mindiff的优化（这将适用于任何基于差异的措施，这些措施关心错误或准确性。它不适用于人口统计）。在指定的公平限制内，与参数过度的同行相比，参数化的Mindiff模型甚至可能具有较低的错误（尽管基线过度参数化模型的错误较低）。我们进一步表明，Mindiff优化对在参数不足的制度中的批处理大小非常敏感。因此，使用Mindiff的公平模型培训需要耗时的超参数搜索。最后，我们建议使用先前提出的正则化技术，即。 L2，与Mindiff结合使用的早期停止和洪水训练公平的参数化模型。

算法公平是一个越来越重要的领域，与检测和减轻机器学习模型中的偏见有关。在回归和分类中，有很多文献来算法公平，但是对生存分析的领域几乎没有探索。生存分析是预测任务，试图预测事件随时间的可能性。生存预测在敏感的环境中尤为重要，例如利用机器学习进行诊断和预后。在本文中，我们探讨了如何利用现有的生存指标来用群体公平指标来衡量偏见。我们在29个生存数据集和8个措施的经验实验中探讨了这一点。我们发现，歧视的度量能够很好地捕捉偏见，而对校准和评分规则的衡量标准则更少。我们建议进一步的研究领域，包括基于预测的公平指标，以进行分配预测。

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

In this work, we tackle two vital tasks in automated driving systems, i.e., driver intent prediction and risk object identification from egocentric images. Mainly, we investigate the question: what would be good road scene-level representations for these two tasks? We contend that a scene-level representation must capture higher-level semantic and geometric representations of traffic scenes around ego-vehicle while performing actions to their destinations. To this end, we introduce the representation of semantic regions, which are areas where ego-vehicles visit while taking an afforded action (e.g., left-turn at 4-way intersections). We propose to learn scene-level representations via a novel semantic region prediction task and an automatic semantic region labeling algorithm. Extensive evaluations are conducted on the HDD and nuScenes datasets, and the learned representations lead to state-of-the-art performance for driver intention prediction and risk object identification.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

This paper presents a simple and effective visual prompting method for adapting pre-trained models to downstream recognition tasks. Our method includes two key designs. First, rather than directly adding together the prompt and the image, we treat the prompt as an extra and independent learnable component. We show that the strategy of reconciling the prompt and the image matters, and find that warping the prompt around a properly shrinked image empirically works the best. Second, we re-introduce two "old tricks" commonly used in building transferable adversarial examples, i.e., input diversity and gradient normalization, into visual prompting. These techniques improve optimization and enable the prompt to generalize better. We provide extensive experimental results to demonstrate the effectiveness of our method. Using a CLIP model, our prompting method sets a new record of 82.8% average accuracy across 12 popular classification datasets, substantially surpassing the prior art by +5.6%. It is worth noting that this prompting performance already outperforms linear probing by +2.1% and can even match fully fine-tuning in certain datasets. In addition, our prompting method shows competitive performance across different data scales and against distribution shifts. The code is publicly available at https://github.com/UCSC-VLAA/EVP.

Named Entity Recognition (NER) is an important and well-studied task in natural language processing. The classic CoNLL-2003 English dataset, published almost 20 years ago, is commonly used to train and evaluate named entity taggers. The age of this dataset raises the question of how well these models perform when applied to modern data. In this paper, we present CoNLL++, a new annotated test set that mimics the process used to create the original CoNLL-2003 test set as closely as possible, except with data collected from 2020. Using CoNLL++, we evaluate the generalization of 20+ different models to modern data. We observe that different models have very different generalization behavior. F\textsubscript{1} scores of large transformer-based models which are pre-trained on recent data dropped much less than models using static word embeddings, and RoBERTa-based and T5 models achieve comparable F\textsubscript{1} scores on both CoNLL-2003 and CoNLL++. Our experiments show that achieving good generalizability requires a combined effort of developing larger models and continuing pre-training with in-domain and recent data. These results suggest standard evaluation methodology may have under-estimated progress on named entity recognition over the past 20 years; in addition to improving performance on the original CoNLL-2003 dataset, we have also improved the ability of our models to generalize to modern data.

We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that violate relevant policies. Our approach extracts structured representations of check-worthy claims, which are aggregated and ranked for review. Stance classifiers are then used to identify tweets supporting novel misinformation claims, which are further reviewed to determine whether they violate relevant policies. To demonstrate the feasibility of our approach, we develop a baseline system based on modern NLP methods for human-in-the-loop fact-checking in the domain of COVID-19 treatments. Using our baseline system, we show that human fact-checkers can identify 124 tweets per hour that violate Twitter's policies on COVID-19 misinformation. We will make our code, data, and detailed annotation guidelines available to support the evaluation of human-in-the-loop systems that identify novel misinformation directly from raw user-generated content.

In the process of materials discovery, chemists currently need to perform many laborious, time-consuming, and often dangerous lab experiments. To accelerate this process, we propose a framework for robots to assist chemists by performing lab experiments autonomously. The solution allows a general-purpose robot to perform diverse chemistry experiments and efficiently make use of available lab tools. Our system can load high-level descriptions of chemistry experiments, perceive a dynamic workspace, and autonomously plan the required actions and motions to perform the given chemistry experiments with common tools found in the existing lab environment. Our architecture uses a modified PDDLStream solver for integrated task and constrained motion planning, which generates plans and motions that are guaranteed to be safe by preventing collisions and spillage. We present a modular framework that can scale to many different experiments, actions, and lab tools. In this work, we demonstrate the utility of our framework on three pouring skills and two foundational chemical experiments for materials synthesis: solubility and recrystallization. More experiments and updated evaluations can be found at https://ac-rad.github.io/arc-icra2023.

Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI value cannot be computed. Estimating AQI values for some point in the future is a predictive process and uses historical AQI values to train and build models. In this work we focus on gap filling in air quality data where the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario is where one or a number of air, weather and traffic sensors are offline and explores prediction accuracy under such situations. The work is part of the MediaEval'2022 Urban Air: Urban Life and Air Pollution task submitted by the DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI, weather and CCTV traffic images for air pollution prediction.