In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like striped and four-legged, one can construct a classifier for the zebra category by enumerating which properties it possesses--even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute's error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.

Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture. The top-down influence is especially effective when dealing with high noise or difficult segmentation problems. Our system is modular and extensible. It is also easy to train and cheap to run, and yet can accommodate complex behaviors. We obtain classification accuracy better than or competitive with state of art results on the MNIST variation dataset, and successfully disentangle overlaid digits with high success rates. We view such a general purpose framework as an essential foundation for a larger system emulating the cognitive abilities of the whole brain.

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on V-statistics, which are degenerate under the null hypothesis, and nondegenerate elsewhere. To illustrate this approach, we construct a two-sample test, an instantaneous independence test and a multiple lag independence test for time series. In experiments, the wild bootstrap gives strong performance on synthetic examples, on audio data, and in performance benchmarking for the Gibbs sampler.

Matching local visual features is a crucial problem in computer vision and its accuracy greatly depends on the choice of similarity measure. As it is generally very difficult to design by hand a similarity or a kernel perfectly adapted to the data of interest, learning it automatically with as few assumptions as possible is preferable. However, available techniques for kernel learning suffer from several limitations, such as restrictive parametrization or scalability. In this paper, we introduce a simple and flexible family of non-linear kernels which we refer to as Quantized Kernels (QK). QKs are arbitrary kernels in the index space of a data quantizer, i.e., piecewise constant similarities in the original feature space. Quantization allows to compress features and keep the learning tractable. As a result, we obtain state-of-the-art matching performance on a standard benchmark dataset with just a few bits to represent each feature dimension. QKs also have explicit non-linear, low-dimensional feature mappings that grant access to Euclidean geometry for uncompressed features.

We thank the reviewers for acknowledging our contributions and providing valuable comments. We'll further improve the paper in the final version. We address the detail comments below. To R1: Q1: Relation with variants of DS: Our main goal is to provide a discriminative max-margin formulation, which is general and complementary to generative methods. For example, though we consider the vanilla DS in CrowdSVM for both clarity and space limit, other variants (e.g., [15,11]) can be naturally incorporated, as the RegBayes formulation (9) is generally applicable to any Bayesian models. Finally, the spectral initialization method [23] for confusion matrices can also be used to initialize the confusion matrices in CrowdSVM, so as the methods in [12].

Large Language and Vision-Language Models (LLMs/VLMs) are increasingly used in safety-critical applications, yet their opaque decision-making complicates risk assessment and reliability. Uncertainty quantification (UQ) helps assess prediction confidence and enables abstention when uncertainty is high. Conformal prediction (CP), a leading UQ method, provides statistical guarantees but relies on static thresholds, which fail to adapt to task complexity and evolving data distributions, leading to suboptimal trade-offs in accuracy, coverage, and informativeness. To address this, we propose learnable conformal abstention, integrating reinforcement learning (RL) with CP to optimize abstention thresholds dynamically. By treating CP thresholds as adaptive actions, our approach balances multiple objectives, minimizing prediction set size while maintaining reliable coverage. Extensive evaluations across diverse LLM/VLM benchmarks show our method outperforms Least Ambiguous Classifiers (LAC) and Adaptive Prediction Sets (APS), improving accuracy by up to 3.2%, boosting AUROC for hallucination detection by 22.19%, enhancing uncertainty-guided selective generation (AUARC) by 21.17%, and reducing calibration error by 70%-85%. These improvements hold across multiple models and datasets while consistently meeting the 90% coverage target, establishing our approach as a more effective and flexible solution for reliable decision-making in safety-critical applications. The code is available at: {https://github.com/sinatayebati/vlm-uncertainty}.

White blood cells (WBC) are important parts of our immune system, and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. The number of WBC types and the total number of WBCs provide important information about our health status. A traditional method, convolutional neural networks (CNN), a deep learning architecture, can classify the blood cell from a part of an object and perform object recognition. Various CNN models exhibit potential; however, their development often involves ad-hoc processes that neglect unnecessary layers, leading to issues with unbalanced datasets and insufficient data augmentation. To address these challenges, we propose a novel ensemble approach that integrates three CNN architectures, each uniquely configured with different dropout and max-pooling layer settings to enhance feature learning. This ensemble model, named DCENWCNet, effectively balances the bias-variance trade-off. When evaluated on the widely recognized Rabbin-WBC dataset, our model outperforms existing state-of-the-art networks, achieving highest mean accuracy. Additionally, it demonstrates superior performance in precision, recall, F1-score, and Area Under the ROC Curve (AUC) across all categories. To delve deeper into the interpretability of classifiers, we employ reliable post-hoc explanation techniques, including Local Interpretable Model-Agnostic Explanations (LIME). These methods approximate the behavior of a black-box model by elucidating the relationships between feature values and predictions. Interpretable results enable users to comprehend and validate the model's predictions, thereby increasing their confidence in the automated diagnosis.

Histogram-Based Outlier Score (HBOS) is a widely used outlier or anomaly detection method known for its computational efficiency and simplicity. However, its assumption of feature independence limits its ability to detect anomalies in datasets where interactions between features are critical. In this paper, we propose the Extended Histogram-Based Outlier Score (EHBOS), which enhances HBOS by incorporating two-dimensional histograms to capture dependencies between feature pairs. This extension allows EHBOS to identify contextual and dependency-driven anomalies that HBOS fails to detect. We evaluate EHBOS on 17 benchmark datasets, demonstrating its effectiveness and robustness across diverse anomaly detection scenarios. EHBOS outperforms HBOS on several datasets, particularly those where feature interactions are critical in defining the anomaly structure, achieving notable improvements in ROC AUC. These results highlight that EHBOS can be a valuable extension to HBOS, with the ability to model complex feature dependencies. EHBOS offers a powerful new tool for anomaly detection, particularly in datasets where contextual or relational anomalies play a significant role.

Machine learning has transformed the classification of biomedical signals such as electrocardiograms (ECGs). Advances in deep learning, particularly convolutional neural networks (CNNs), enable automatic feature extraction, raising the question: Can combining time- and frequency-domain attributes enhance classification accuracy? To explore this, we evaluated three ECG classification tasks: (1) arrhythmia classification, (2) identity recognition, and (3) apnea detection. We initially tested three methods: (i) 2-D spectrogram-based frequency-time classification (SPECT), (ii) time-domain classification using a 1-D CNN (CNN1D), and (iii) frequency-domain classification using a Fourier transform-based CNN (FFT1D). Performance was validated using K-fold cross-validation. Among these, CNN1D (time only) performed best, followed by SPECT (time-frequency) and FFT1D (frequency only). Surprisingly, SPECT, which integrates time- and frequency-domain features, performed worse than CNN1D, suggesting a need for a more effective time and frequency fusion approach. To address this, we tested the recently proposed Fourier Analysis Network (FAN), which combines time- and frequency-domain features. However, FAN performed comparably to CNN1D, excelling in some tasks while underperforming in others. To enhance this approach, we developed the Convolutional Fourier Analysis Network (CFAN), which integrates FAN with CNN. CFAN outperformed all previous methods across all classification tasks. These findings underscore the advantages of combining time- and frequency-domain features, demonstrating CFAN's potential as a powerful and versatile solution for ECG classification and broader biomedical signal analysis

Additional Feedback: It would be interesting to see a discussion of how this work lies in comparison to classes of knowledge bases that enable tractable abductive reasoning [1]. For example, is this result a special case of some known class/language? I just wanted to address the author's request for specific references "that might cast doubt on the novelty of our work". Sorry for not being more concrete, but here are some specific references. David Eppstein The polynomial time enumeration algorithm proposed for Eq 16 is basically subset sum where we enumerate all subsets that sum less than some threshold.