A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers

Many real-world applications of tabular data involve using historic events to predict properties of new ones, for example whether a credit card transaction is fraudulent or what rating a customer will assign a product on a retail platform. Existing approaches to event prediction include costly, brittle, and application-dependent techniques such as time-aware positional embeddings, learned row and field encodings, and oversampling methods for addressing class imbalance. Moreover, these approaches often assume specific use-cases, for example that we know the labels of all historic events or that we only predict a pre-specified label and not the data's features themselves. In this work, we propose a simple but flexible baseline using standard autoregressive LLM-style transformers with elementary positional embeddings and a causal language modeling objective. Our baseline outperforms existing approaches across popular datasets and can be employed for various use-cases. We demonstrate that the same model can predict labels, impute missing values, or model event sequences.

Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning

Vision transformers have established a precedent of patchifying images into uniformly-sized chunks before processing. We hypothesize that this design choice may limit models in learning comprehensive and compositional representations from visual data. This paper explores the notion of providing semantically-meaningful visual tokens to transformer encoders within a vision-language pre-training framework. Leveraging off-the-shelf segmentation and scene-graph models, we extract representations of instance segmentation masks (referred to as tangible tokens) and relationships and actions (referred to as intangible tokens). Subsequently, we pre-train a vision-side transformer by incorporating these newly extracted tokens and aligning the resultant embeddings with caption embeddings from a text-side encoder. To capture the structural and semantic relationships among visual tokens, we introduce additive attention weights, which are used to compute self-attention scores. Our experiments on COCO demonstrate notable improvements over ViTs in learned representation quality across text-to-image (+47%) and image-to-text retrieval (+44%) tasks. Furthermore, we showcase the advantages on compositionality benchmarks such as ARO (+18%) and Winoground (+10%).

From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management

Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, surveillance, to name a few. While the literature is abundant in effective detection algorithms due to this practical relevance, autonomous anomaly detection is rarely used in real-world scenarios. Especially in high-stakes applications, a human-in-the-loop is often involved in processes beyond detection such as verification and troubleshooting. In this work, we introduce ALARM (for Analyst-in-the-Loop Anomaly Reasoning and Management); an end-to-end framework that supports the anomaly mining cycle comprehensively, from detection to action. Besides unsupervised detection of emerging anomalies, it offers anomaly explanations and an interactive GUI for human-in-the-loop processes -- visual exploration, sense-making, and ultimately action-taking via designing new detection rules -- that help close ``the loop'' as the new rules complement rule-based supervised detection, typical of many deployed systems in practice. We demonstrate \method's efficacy through a series of case studies with fraud analysts from the financial industry.