Few-shot learning (FSL) is a central problem in meta-learning, where learners must efficiently learn from few labeled examples. Within FSL, feature pre-training has recently become an increasingly popular strategy to significantly improve generalization performance. However, the contribution of pre-training is often overlooked and understudied, with limited theoretical understanding of its impact on meta-learning performance. Further, pre-training requires a consistent set of global labels shared across training tasks, which may be unavailable in practice. In this work, we address the above issues by first showing the connection between pre-training and meta-learning. We discuss why pre-training yields more robust meta-representation and connect the theoretical analysis to existing works and empirical results. Secondly, we introduce Meta Label Learning (MeLa), a novel meta-learning algorithm that learns task relations by inferring global labels across tasks. This allows us to exploit pre-training for FSL even when global labels are unavailable or ill-defined. Lastly, we introduce an augmented pre-training procedure that further improves the learned meta-representation. Empirically, MeLa outperforms existing methods across a diverse range of benchmarks, in particular under a more challenging setting where the number of training tasks is limited and labels are task-specific. We also provide extensive ablation study to highlight its key properties.

Francesca Mani and her mother Dorota join'The Ingraham Angle' to demand accountability for victims. Francesca Mani told "The Ingraham Angle" that the principal at Westfield High School recently notified her that she was one of multiple victims. "After that, I just felt, like, betrayed because I never thought it'd be my classmate, and when I came home, I told my mom and I said, 'We need to do something about this because it's not OK, and people are making it seem like it is.'" Mani said she never personally witnessed the explicit images, but that she felt betrayed. Mani said she believes she knows who the main culprit in the dissemination of the images is, but did not mention their name on air.

The integration of complex morphological, syntactic, semantic, and Automatic Readability Assessment (ARA) is an discourse features in modern ARA approaches offers important task in computational linguistics that the possibility of significantly improving the aims to automatically determine the level of difficulty current readability studies in Turkish. In this paper, of understanding a written text, which has we present the first ARA study for Turkish. Our implications for various fields, such as healthcare, study combines traditional raw text features with education, and accessibility (Vajjala, 2021). In lexical, morpho-syntactic, and syntactic information the healthcare sector, medical practitioners can use to create an advanced readability assessment ARA tools to ensure patient information and consent tool for Turkish. We demonstrate the effectiveness forms are easily understandable (Ley and Florio, of our tool on a new corpus of Turkish popular 1996). In the field of education, teachers and science magazine articles, published for different learners alike can benefit from ARA systems to age groups and educational levels. Our study adapt materials to the appropriate language proficiency aims to contribute to the development of automated level (Kintsch and Vipond, 2014).

Spoken language understanding systems using audio-only data are gaining popularity, yet their ability to handle unseen intents remains limited. In this study, we propose a generalized zero-shot audio-to-intent classification framework with only a few sample text sentences per intent. To achieve this, we first train a supervised audio-to-intent classifier by making use of a self-supervised pre-trained model. We then leverage a neural audio synthesizer to create audio embeddings for sample text utterances and perform generalized zero-shot classification on unseen intents using cosine similarity. We also propose a multimodal training strategy that incorporates lexical information into the audio representation to improve zero-shot performance. Our multimodal training approach improves the accuracy of zero-shot intent classification on unseen intents of SLURP by 2.75% and 18.2% for the SLURP and internal goal-oriented dialog datasets, respectively, compared to audio-only training.

In Programming by Demonstration, the robot learns novel skills from human demonstrations. After learning, the robot should be able not only to reproduce the skill, but also to generalize it to shifted domains without collecting new training data. Adaptation to similar domains has been investigated in the literature; however, an open problem is how to adapt learned skills to different conditions that are outside of the data distribution, and, more important, how to preserve the precision of the desired adaptations. This paper presents a novel supervised learning framework called Constrained Equation Learner Networks that addresses the trajectory adaptation problem in Programming by Demonstrations from a constrained regression perspective. While conventional approaches for constrained regression use one kind of basis function, e.g., Gaussian, we exploit Equation Learner Networks to learn a set of analytical expressions and use them as basis functions. These basis functions are learned from demonstration with the objective to minimize deviations from the training data while imposing constraints that represent the desired adaptations, like new initial or final points or maintaining the trajectory within given bounds. Our approach addresses three main difficulties in adapting robotic trajectories: 1) minimizing the distortion of the trajectory for new adaptations; 2) preserving the precision of the adaptations; and 3) dealing with the lack of intuition about the structure of basis functions. We validate our approach both in simulation and in real experiments in a set of robotic tasks that require adaptation due to changes in the environment, and we compare obtained results with two existing approaches. Performed experiments show that Constrained Equation Learner Networks outperform state of the art approaches by increasing generalization and adaptability of robotic skills.

In this paper, a novel Follow-the-Perturbed-Leader type algorithm is proposed and analyzed for solving general long-term constrained optimization problems in online manner, where the objective and constraints are not necessarily convex. In each period, random linear perturbation and strongly concave perturbation are incorporated in primal and dual directions, respectively, to the offline oracle, and a global minimax point is searched as solution. Based on two particular definitions of expected static cumulative regret, we derive the first sublinear $O(T^{8/9})$ regret complexity for this class of problems. The proposed algorithm is applied to tackle a long-term (risk) constrained river pollutant source identification problem, demonstrating the validity of the theoretical results and exhibiting superior performance compared to existing method.

Recently, the Deep Learning community has become interested in evolutionary optimization (EO) as a means to address hard optimization problems, e.g. meta-learning through long inner loop unrolls or optimizing non-differentiable operators. One core reason for this trend has been the recent innovation in hardware acceleration and compatible software - making distributed population evaluations much easier than before. Unlike for gradient descent-based methods though, there is a lack of hyperparameter understanding and best practices for EO - arguably due to severely less 'graduate student descent' and benchmarking being performed for EO methods. Additionally, classical benchmarks from the evolutionary community provide few practical insights for Deep Learning applications. This poses challenges for newcomers to hardware-accelerated EO and hinders significant adoption. Hence, we establish a new benchmark of EO methods (NeuroEvoBench) tailored toward Deep Learning applications and exhaustively evaluate traditional and meta-learned EO. We investigate core scientific questions including resource allocation, fitness shaping, normalization, regularization & scalability of EO. The benchmark is open-sourced at https://github.com/neuroevobench/neuroevobench under Apache-2.0 license.

Reinforcement Learning (RL) has shown its power in solving sequential decision-making problems in the robotic domain [1, 2], through optimizing control policies directly from trial-and-error interactions with environments. However, there are still several challenges [3], like sample inefficiency and difficulties in specifying rewards, limiting its applications to the field. Inspired by how we human beings learn skills from more knowledgeable persons such as teachers or supervisors, a potential solution for the above limitations is learning from human expert guidance, so as to inject additional information into the learning process. Human guidance has shown some benefits in terms of providing additional rewards or guidance to accelerate the learning of new tasks, including learning from human demonstrations [4, 5] and feedback [6, 7, 8, 9]. However, collecting sufficient human guidance is time-consuming and costly. Recently, Large Language Models (LLMs) have shown remarkable abilities to generate human-like responses in the textual domain [10, 11], and their applications have been explored in the robotic domain. While some approaches prompt LLMs to instruct robots in performing tasks [12, 13, 14, 15], they focus on utilizing LLMs' common-sense knowledge to give high-level advice for employing pre-trained or hard-coded low-level control policies, which requires much data collection or expert knowledge respectively. Since these works do not perform policy learning when executing tasks with LLMs, the robots' performance highly depends on the LLM's capabilities and consistent presence during the interactions each time tasks are executed.

Building a question answering (QA) model with less annotation costs can be achieved by utilizing active learning (AL) training strategy. It selects the most informative unlabeled training data to update the model effectively. Acquisition functions for AL are used to determine how informative each training example is, such as uncertainty or diversity based sampling. In this work, we propose a perturbation-based active learning acquisition strategy and demonstrate it is more effective than existing commonly used strategies.

Having good English writing skills is extremely important for students in polytechnic institutions. However, a lot of students in technical schools have difficulties in reaching high levels of skill. The Input Hypothesis, created by Stephen Krashen, suggests that people learn languages well when they receive information that's a little harder than what they already know but still understandable. This research paper wants to study how the Input Hypothesis can help polytechnic students improve their English writing skills. The study will include real-life observations and experiments from the previous research. We will look at data from polytechnic students who are receiving special writing instruction to see if the Input Hypothesis actually helps improve their writing skills. The paper can better inform polytechnic students, faculty members, and support staff and even members of the larger community about the attributions, the processes, and the possible outcomes of second language development for polytechnic students.