Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 25 for “"Softmax"”.
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Utilizing network features to detect erroneous inputs
… types of erroneous data using the hidden and softmax feature vectors of pre-trained neural networks. Results indicate that these faulty data types generally exhibit linearly separable activation properties from correctly processed examples. I am able to identify erroneous inputs with an AUROC …
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EFFORTS TOWARD TRUSTWORTHY MACHINE LEARNING: MITIGATING OVERCONFIDENCE, HALLUCINATION, AND MODALITY BIAS
… offering more reliable confidence estimates than softmax scores. We further explore self-supervised probing tasks to capture semantic signals of correctness, improving calibration and failure detection on in- and out-of-distribution data. Second, we tackle hallucinations in large vision-language …
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Low-shot Visual Recognition
… the proposed solution performs better than the softmax classifier by a good margin.
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Random Features for Efficient Attention Approximation
… linear O(L) via an unbiased approximation of the softmax kernel appearing in self-attention, the main component in the Transformer backbone. The obtained efficient Transformer architecture is referred to as Performer. Compared to other developments in the area of efficient Transformers, Performer …
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A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.
… of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, …
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A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques
… of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, …
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A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.
… of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, …
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Addressing the Interclass Similarity Challenges in Automated Face Recognition
… a fine-tuned framework, incorporating the Softmax activation function as the final layer in advanced face recognition models. This final layer predicts whether input images depict the same or different individuals, mitigating challenges associated with threshold variations. Overall, this …
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A Minimax Approach for Learning Gaussian Mixtures
… in GAT-GMM using a random linear generator and a softmax-based quadratic discriminator architecture, which leads to a non-convex concave minimax optimization problem. We show that a Gradient Descent Ascent (GDA) method converges to an approximate stationary minimax point of the GAT-GMM …
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Learning Probabilistic Generative Models For Fast Sampling-Based Planning
… from which to extend the search tree via the softmax function of learned state values. We also discuss a novel constrained sampling-based motion planning method for grasp and transport tasks with redundant robotic manipulators, which allows the best grasp configuration and approach direction …
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Exploring anomaly detection methods using features extracted with a neural network
… often outperforms the use of the maximum softmax probability (MSP) as an anomaly score. We also demonstrate that the features extracted using neural networks often lead to better anomaly detection performance than the features obtained by simply reducing the dimensions of the input images …
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Classifying Challenging Behaviors in Autism Spectrum Disorder with Neural Document Embeddings
… Doc2Vec embeddings which use hierarchical softmax as an output layer, and Doc2Vec which optimizes the original Doc2Vec architecture through Negative Sampling. Once created, these embeddings are initially used as input to a Support Vector Machine classifier to demonstrate the success of …
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DL-DI: A Deep Learning Framework for Distributed, Incremental Image Classification
… DL-DI framework on image classification using Softmax Regression and Convolutional Neural Networks on MNIST, CIFAR10 datasets. The evaluation results have verified that the DL-DIS framework supports distributed incremental Deep Learning while achieving a reasonably high rate of prediction …
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Distributed learning in games under bounded rationality
… theoretical contributions, we define independent softmax dynamics as a learning model and illustrate their behavior through simulations across the studied subclasses. Taken together, these results deepen our understanding of how distributed, boundedly rational agents, each acting on limited …
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Advances in Probabilistic Modelling: Sparse Gaussian Processes, Autoencoders, and Few-shot Learning
… and can be linked to automatically regularised softmax regression. The second employs an amortised head model; it can be viewed to meta-learn probabilistic inference for prediction, and can be generalised to other contexts such as few-shot regression.
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Attention-Based Encoder-Decoder Models for Speech Processing
… dataset showed that compared to using Softmax probabilities as confidence scores, the CEM improved token-level confidence estimation performance substantially and largely addressed the over-confidence issue. For various downstream tasks such as data selection, utterance-level confidence …
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Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach
… Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation.
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DeepGeoMap
… layers and utilizes rectified linear unit and softmax activation, 1D max and 1D global average pooling layers, additional dropout to prevent overfitting, and a categorical cross-entropy loss function with Adam gradient descent optimization. DeepGeoMap was realized using Python 3.7 and the …
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Exploring Loss Functions in Machine Learning
… three new loss functions and their applications. Softmax Cross-Entropy Loss, stands as a prevalent choice in neural network classification tasks. It treats all misclassifications uniformly. However, multi-class classification problems often have many semantically similar classes. We should expect …
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Robustness To Visual Perturbations In Pixel-Based Tasks
… derive a simple extension to current softmax-linear models, which learns to disentangle the two components during training. On several common vision models, the disentangled model outperforms other calibration methods on standard calibration metrics in the face of out-of-distribution …
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