Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process.

Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled dataset by self-annotating the potential samples during the training process. Several works have shown that it can improve the graph learning model performance in general. However, we notice that the incorrect labels can be fatal to the graph training process. Inappropriate PL may result in the performance degrading, especially on graph data where the noise can propagate. Surprisingly, the corresponding error is seldom theoretically analyzed in the literature.

The nonparametric learning of positive-valued functions appears widely in machine learning, especially in the context of estimating intensity functions of point processes. Yet, existing approaches either require computing expensive projections or semidefinite relaxations, or lack convexity and theoretical guarantees after introducing nonlinear link functions. In this paper, we propose a novel algorithm, pseudo mirror descent, that performs efficient estimation of positive functions within a Hilbert space without expensive projections. The algorithm guarantees positivity by performing mirror descent with an appropriately selected Bregman divergence, and a pseudo-gradient is adopted to speed up the gradient evaluation procedure in practice. We analyze both asymptotic and nonasymptotic convergence of the algorithm. Through simulations, we show that pseudo mirror descent outperforms the state-of-the-art benchmarks for learning intensities of Poisson and multivariate Hawkes processes, in terms of both computational efficiency and accuracy.

Neural prediction offers a promising approach to forecasting the individual variability of neurocognitive functions and disorders and providing prognostic indicators for personalized invention. However, it is challenging to translate neural predictive models into medical artificial intelligent applications due to the limitations of domain shift and label scarcity. Here, we propose the directed evolution model (DEM), a novel computational model that mimics the trial-and-error processes of biological directed evolution to approximate optimal solutions for predictive modeling tasks. We demonstrated that the directed evolution algorithm is an effective strategy for uncertainty exploration, enhancing generalization in reinforcement learning. Furthermore, by incorporating replay buffer and continual backpropagate methods into DEM, we provide evidence of achieving better trade-off between exploitation and exploration in continuous learning settings. We conducted experiments on four different datasets for children with cochlear implants whose spoken language developmental outcomes vary considerably on the individual-child level. Preoperative neural MRI data has shown to accurately predict the post-operative outcome of these children within but not across datasets. Our results show that DEM can efficiently improve the performance of cross-domain pre-implantation neural predictions while addressing the challenge of label scarcity in target domain.

This paper proposes a new enhanced model architecture to perform classification of lumbar spine degeneration with DICOM images while using a hybrid approach, integrating EfficientNet and VGG19 together with custom-designed components. The proposed model is differentiated from traditional transfer learning methods as it incorporates a Pseudo-Newton Boosting layer along with a Sparsity-Induced Feature Reduction Layer that forms a multi-tiered framework, further improving feature selection and representation. The Pseudo-Newton Boosting layer makes smart variations of feature weights, with more detailed anatomical features, which are mostly left out in a transfer learning setup. In addition, the Sparsity-Induced Layer removes redundancy for learned features, producing lean yet robust representations for pathology in the lumbar spine. This architecture is novel as it overcomes the constraints in the traditional transfer learning approach, especially in the high-dimensional context of medical images, and achieves a significant performance boost, reaching a precision of 0.9, recall of 0.861, F1 score of 0.88, loss of 0.18, and an accuracy of 88.1%, compared to the baseline model, EfficientNet. This work will present the architectures, preprocessing pipeline, and experimental results. The results contribute to the development of automated diagnostic tools for medical images.

We thank all reviewers for their comments and acknowledgement of our contribution. Theorem 3 and Corollary 4, as Reviewer 3 suggested. How to choose the proper Bregman divergence? It is yet unclear whether there exist ways to systematically design the "best Bregman divergence in a (k 1) This is also commonly adopted in the literature. Is continuity of the intensity function restrictive?

We provide an algorithm table that represents HIGL in Algorithm 1. Algorithm 1 Hierarchical reinforcement learning guided by landmarks (HIGL)Input: Goal transition function h, state-goal mapping function ϕ, high-level action frequency m, RND networks θ, θ Initialize empty adjacency matrix M Initialize priority queue Q for n = 1,...,N do Reset the environment and sample the initial state s Sample episode end signal done . Build a graph with the sampled landmarks, a state and a goal. A simulated ball (point mass) starts at the bottom left corner in a " "-shaped maze and aims to reach the top left corner. Its actions correspond to torques applied to joints. This environment has a " "-shaped maze whose size is We define a "success" as being within an L2 distance Each episode is terminated if the agent reaches the goal or after 500 steps.

Here we describe the implementation details for fine-tuning the pre-trained model. We consider two datasets for this task: COCO and Flickr30K. We follow the original dataset split with 29.8k images for training, 1k for evaluation, and 1k for test. It contains 83k images for training, 41k for validation, and 81k for test. Quantitative comparison between ITC and ITM is shown in Table 5. Figure 7 shows the qualitative "small black bag" "the larger black suitcase" "elephant with trunk curled" "elephant with trunk up" ITC ITM Grad-CAMs from the multimodal encoder capture finer-grained details such as "larger" and "curled".