This paper seeks to address the dense labeling problems where a significant fraction of the dataset can be pruned without sacrificing much accuracy. We observe that, on standard medical image segmentation benchmarks, the loss gradient norm-based metrics of individual training examples applied in image classification fail to identify the important samples. To address this issue, we propose a data pruning method by taking into consideration the training dynamics on target regions using Dynamic Average Dice (DAD) score. To the best of our knowledge, we are among the first to address the data importance in dense labeling tasks in the field of medical image analysis, making the following contributions: (1) investigating the underlying causes with rigorous empirical analysis, and (2) determining effective data pruning approach in dense labeling problems. Our solution can be used as a strong yet simple baseline to select important examples for medical image segmentation with combined data sources.

Data scarcity has been the main factor that hinders the progress of event extraction. To overcome this issue, we propose a Self-Training with Feedback (STF) framework that leverages the large-scale unlabeled data and acquires feedback for each new event prediction from the unlabeled data by comparing it to the Abstract Meaning Representation (AMR) graph of the same sentence. Specifically, STF consists of (1) a base event extraction model trained on existing event annotations and then applied to large-scale unlabeled corpora to predict new event mentions as pseudo training samples, and (2) a novel scoring model that takes in each new predicted event trigger, an argument, its argument role, as well as their paths in the AMR graph to estimate a compatibility score indicating the correctness of the pseudo label. The compatibility scores further act as feedback to encourage or discourage the model learning on the pseudo labels during self-training. Experimental results on three benchmark datasets, including ACE05-E, ACE05-E+, and ERE, demonstrate the effectiveness of the STF framework on event extraction, especially event argument extraction, with significant performance gain over the base event extraction models and strong baselines. Our experimental analysis further shows that STF is a generic framework as it can be applied to improve most, if not all, event extraction models by leveraging large-scale unlabeled data, even when high-quality AMR graph annotations are not available.

When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Direct visualization of embeddings (right; top three PCA directions shown) confirms that embeddings are initially collapsed to a point, which then expands to a 1D manifold, a 2D manifold, and beyond concurrently with steps in the loss. It is widely believed that deep learning's stunning success is due in part to its ability to discover and extract useful representations of complex data. Self-supervised learning (SSL) has emerged as a leading framework for learning these representations for images directly from unlabeled data, similar to how LLMs learn representations for language directly from web-scraped text. Yet despite SSL's key role in state-of-the-art models such as CLIP and MidJourney, fundamental questions like "what are self-supervised image systems really learning?" and "how does that learning actually occur?"

Large language models (LLMs) have shown remarkable However, despite the advantages of ICL, it is still unclear how ICL capacity for in-context learning (ICL), where learning a new task learns knowledge from the given prompts without updating its model from just a few training examples is done without being explicitly parameters. Preliminary research [1, 11] compared ICL with simple pre-trained. However, despite the success of LLMs, there has been machine learning models, such as logistic regression and shallow little understanding of how ICL learns the knowledge from the given neural networks. In this paper, we take a further step and investigate prompts. In this paper, to make progress toward understanding the learning behaviour differences between ICL and supervised learning learning behaviour of ICL, we train the same LLMs with the same (SL). Specifically, we train three LLMs with the same training data demonstration examples via ICL and supervised learning (SL), respectively, via in-context learning and supervised learning separately and analyze and investigate their performance under label perturbations their generated outputs. While SL is a well-established approach (i.e., noisy labels and label imbalance) on a range of classification that uses labelled data to train models to make accurate predictions, tasks. First, via extensive experiments, we find that gold labels ICL takes a different approach by leveraging the context of the text have significant impacts on the downstream in-context performance, to learn from unlabeled data in order to improve the accuracy of the especially for large language models; however, imbalanced predictions. By comparing the performance of ICL and SL, we gain labels matter little to ICL across all model sizes.

Motivated by the need to address the degeneracy of canonical Laplace learning algorithms in low label rates, we propose to reformulate graph-based semi-supervised learning as a nonconvex generalization of a \emph{Trust-Region Subproblem} (TRS). This reformulation is motivated by the well-posedness of Laplacian eigenvectors in the limit of infinite unlabeled data. To solve this problem, we first show that a first-order condition implies the solution of a manifold alignment problem and that solutions to the classical \emph{Orthogonal Procrustes} problem can be used to efficiently find good classifiers that are amenable to further refinement. Next, we address the criticality of selecting supervised samples at low-label rates. We characterize informative samples with a novel measure of centrality derived from the principal eigenvectors of a certain submatrix of the graph Laplacian. We demonstrate that our framework achieves lower classification error compared to recent state-of-the-art and classical semi-supervised learning methods at extremely low, medium, and high label rates. Our code is available on github\footnote{anonymized for submission}.

Limited availability of labeled data for machine learning on biomedical time-series hampers progress in the field. Self-supervised learning (SSL) is a promising approach to learning data representations without labels. However, current SSL methods require expensive computations for negative pairs and are designed for single modalities, limiting their versatility. To overcome these limitations, we introduce CroSSL (Cross-modal SSL). CroSSL introduces two novel concepts: masking intermediate embeddings from modality-specific encoders and aggregating them into a global embedding using a cross-modal aggregator. This enables the handling of missing modalities and end-to-end learning of cross-modal patterns without prior data preprocessing or time-consuming negative-pair sampling. We evaluate CroSSL on various multimodal time-series benchmarks, including both medical-grade and consumer biosignals. Our results demonstrate superior performance compared to previous SSL techniques and supervised benchmarks with minimal labeled data. We additionally analyze the impact of different masking ratios and strategies and assess the robustness of the learned representations to missing modalities. Overall, our work achieves state-of-the-art performance while highlighting the benefits of masking latent embeddings for cross-modal learning in temporal health data.

This paper presents a novel method for model-free prediction of grasp poses for suction grippers with multiple suction cups. Our approach is agnostic to the design of the gripper and does not require gripper-specific training data. In particular, we propose a two-step approach, where first, a neural network predicts pixel-wise grasp quality for an input image to indicate areas that are generally graspable. Second, an optimization step determines the optimal gripper selection and corresponding grasp poses based on configured gripper layouts and activation schemes. In addition, we introduce a method for automated labeling for supervised training of the grasp quality network. Experimental evaluations on a real-world industrial application with bin picking scenes of varying difficulty demonstrate the effectiveness of our method.

2D forward-looking sonar is a crucial sensor for underwater robotic perception. A well-known problem in this field is estimating missing information in the elevation direction during sonar imaging. There are demands to estimate 3D information per image for 3D mapping and robot navigation during fly-through missions. Recent learning-based methods have demonstrated their strengths, but there are still drawbacks. Supervised learning methods have achieved high-quality results but may require further efforts to acquire 3D ground-truth labels. The existing self-supervised method requires pretraining using synthetic images with 3D supervision. This study aims to realize stable self-supervised learning of elevation angle estimation without pretraining using synthetic images. Failures during self-supervised learning may be caused by motion degeneracy problems. We first analyze the motion field of 2D forward-looking sonar, which is related to the main supervision signal. We utilize a modern learning framework and prove that if the training dataset is built with effective motions, the network can be trained in a self-supervised manner without the knowledge of synthetic data. Both simulation and real experiments validate the proposed method.

In recent years, self-supervised learning (SSL) frameworks have been extensively applied to sensor-based Human Activity Recognition (HAR) in order to learn deep representations without data annotations. While SSL frameworks reach performance almost comparable to supervised models, studies on interpreting representations learnt by SSL models are limited. Nevertheless, modern explainability methods could help to unravel the differences between SSL and supervised representations: how they are being learnt, what properties of input data they preserve, and when SSL can be chosen over supervised training. In this paper, we aim to analyze deep representations of two recent SSL frameworks, namely SimCLR and VICReg. Specifically, the emphasis is made on (i) comparing the robustness of supervised and SSL models to corruptions in input data; (ii) explaining predictions of deep learning models using saliency maps and highlighting what input channels are mostly used for predicting various activities; (iii) exploring properties encoded in SSL and supervised representations using probing. Extensive experiments on two single-device datasets (MobiAct and UCI-HAR) have shown that self-supervised learning representations are significantly more robust to noise in unseen data compared to supervised models. In contrast, features learnt by the supervised approaches are more homogeneous across subjects and better encode the nature of activities.

Hierarchical multi-label text classification aims to classify the input text into multiple labels, among which the labels are structured and hierarchical. It is a vital task in many real world applications, e.g. scientific literature archiving. In this paper, we survey the recent progress of hierarchical multi-label text classification, including the open sourced data sets, the main methods, evaluation metrics, learning strategies and the current challenges. A few future research directions are also listed for community to further improve this field.