In this work we apply a texture classification network to remote sensing im(cid:173) age analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probability estimates for the output classes. We describe the texture classification network and extend it to demonstrate its application to the Landsat and Aerial image analysis domain .

We demonstrate in this paper how certain forms of rule-based knowledge can be used to prestructure a neural network of nor(cid:173) malized basis functions and give a probabilistic interpretation of the network architecture. We describe several ways to assure that rule-based knowledge is preserved during training and present a method for complexity reduction that tries to minimize the num(cid:173) ber of rules and the number of conjuncts. After training the refined rules are extracted and analyzed.

Imagine you have designed a neural network that successfully learns a complex classification task. What are the relevant input features the classifier relies on and how are these features combined to pro(cid:173) duce the classification decisions? There are applications where a deeper insight into the structure of an adaptive system and thus into the underlying classification problem may well be as important as the system's performance characteristics, e.g. in economics or medicine. GDSi is a backpropagation-based training scheme that produces networks transformable into an equivalent and concise set of IF-THEN rules. This is achieved by imposing penalty terms on the network parameters that adapt the network to the expressive power of this class of rules.

Casting neural network weights in symbolic terms is crucial for interpreting and explaining the behavior of a network. Additionally, in some domains, a symbolic description may lead to more robust generalization. We present a principled approach to symbolic rule extraction based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to a unit's actual weights. Depending on the requirements of the application domain, our method can accommodate arbitrary disjunctions and conjunctions with O(k) complexity, simple n-of-m expressions with O( k!) complexity, or a more general class of recursive n-of-m expressions with O(k!) complexity, where k is the number of inputs to a unit.

Classifier systems are now viewed disappointing because of their prob(cid:173) lems such as the rule strength vs rule set performance problem and the credit assignment problem. In order to solve the problems, we have de(cid:173) veloped a hybrid classifier system: GLS (Generalization Learning Sys(cid:173) tem). In designing GLS, we view CSs as model free learning in POMDPs and take a hybrid approach to finding the best generalization, given the total number of rules. GLS uses the policy improvement procedure by Jaakkola et al. for an locally optimal stochastic policy when a set of rule conditions is given. GLS uses GA to search for the best set of rule conditions.

We explore the consequences of viewing semantic association as the result of attempting to predict the concepts likely to arise in a particular context. We argue that the success of existing accounts of semantic representation comes as a result of indirectly addressing this problem, and show that a closer correspondence to human data can be obtained by taking a probabilistic approach that explicitly models the generative structure of language.

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.

Abstract-- We focus on the task of language-conditioned object placement, in which a robot should generate placements that satisfy all the spatial relational constraints in language instructions. Previous works based on rule-based language parsing or scene-centric visual representation have restrictions on the form of instructions and reference objects or require large amounts of training data. We propose an object-centric framework that leverages foundation models to ground the reference objects and spatial relations for placement, which is more sample efficient and generalizable. Experiments indicate that our model can achieve a 97.75% success rate of placement with only 0.26M trainable parameters. Object placement is an essential task in human-robot contains only one object in the scene and does not support interaction.

Charts are a powerful tool for visually conveying complex data, but their comprehension poses a challenge due to the diverse chart types and intricate components. Existing chart comprehension methods suffer from either heuristic rules or an over-reliance on OCR systems, resulting in suboptimal performance. To address these issues, we present ChartReader, a unified framework that seamlessly integrates chart derendering and comprehension tasks. Our approach includes a transformer-based chart component detection module and an extended pre-trained vision-language model for chart-to-X tasks. By learning the rules of charts automatically from annotated datasets, our approach eliminates the need for manual rule-making, reducing effort and enhancing accuracy.~We also introduce a data variable replacement technique and extend the input and position embeddings of the pre-trained model for cross-task training. We evaluate ChartReader on Chart-to-Table, ChartQA, and Chart-to-Text tasks, demonstrating its superiority over existing methods. Our proposed framework can significantly reduce the manual effort involved in chart analysis, providing a step towards a universal chart understanding model. Moreover, our approach offers opportunities for plug-and-play integration with mainstream LLMs such as T5 and TaPas, extending their capability to chart comprehension tasks. The code is available at https://github.com/zhiqic/ChartReader.

We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. We draw on recent advances in guided diffusion modeling to achieve test-time controllability of trajectories, which is normally only associated with rule-based systems. Our guided diffusion model allows users to constrain trajectories through target waypoints, speed, and specified social groups while accounting for the surrounding environment context. This trajectory diffusion model is integrated with a novel physics-based humanoid controller to form a closed-loop, full-body pedestrian animation system capable of placing large crowds in a simulated environment with varying terrains. We further propose utilizing the value function learned during RL training of the animation controller to guide diffusion to produce trajectories better suited for particular scenarios such as collision avoidance and traversing uneven terrain. Video results are available on the project page at https://nv-tlabs.github.io/trace-pace .