课程学习是一种强大的培训方法，可以在某些情况下更快，更好的培训。但是，这种方法需要一个概念，即哪些示例很难且容易，这并不总是很容易提供。最近称为C得分的度量标准将其作为代理，例如，将其与学习一致性联系起来。不幸的是，这种方法是相当大的强化，从而限制了其对替代数据集的适用性。在这项工作中，我们通过不同的方法训练模型，以预测CIFAR-100和CIFAR-10的C得分。但是，我们发现这些模型在相同的分布和分布不足之内都概括了。这表明C分数不是由每个样本的个体特征定义的，而是由其他因素定义的。我们假设样本与其邻居的关系，尤其是其中有多少人共享相同的标签，可以帮助解释C分数。我们计划在未来的工作中探索这一点。

持续的学习方法努力减轻灾难性遗忘（CF），在学习新任务时，从以前学习的任务中丢失了知识。在这些算法中，有些在训练时维护以前任务中的样本子集。这些样本称为内存。这些方法表现出出色的性能，同时在概念上简单易于实现。然而，尽管它们很受欢迎，但几乎没有做任何事情来理解要包含在记忆中的元素。当前，这种记忆通常是通过随机抽样填充的，没有指导原则可以有助于保留以前的知识。在这项工作中，我们提出了一个基于称为一致性意识采样（CAWS）的样本的学习一致性的标准。该标准优先考虑通过深网更容易学习的样本。我们对三种不同的基于内存的方法进行研究：AGEM，GDUMB和经验重播，在MNIST，CIFAR-10和CIFAR-100数据集上。我们表明，使用最一致的元素在受到计算预算的约束时会产生性能提高；如果在没有这种约束的情况下，随机抽样是一个强大的基线。但是，在经验重播上使用CAWS可以改善随机基线的性能。最后，我们表明CAWS取得了与流行的内存选择方法相似的结果，同时需要大大减少计算资源。

当随着时间的推移学习任务时，人工神经网络遭受称为灾难性遗忘（CF）的问题。当在训练网络的训练过程中覆盖网络的权重，导致忘记旧信息的新任务时，会发生这种情况。为了解决这个问题，我们提出了META可重复使用的知识或标记，这是一种新的方法，可以在学习新任务时促进重量可重用性而不是覆盖。具体来说，标记在任务之间保留一组共享权重。我们将这些共享权重设定为共同的知识库（KB），不仅用于学习新任务，而且还富有以丰富的新知识，因为模型了解新任务。标记背后的关键组件是两倍。一方面，冶金学习方法提供了逐步丰富KB的关键机制，并在任务之间促进重量可重用性。另一方面，一组培训掩模提供了选择性地从KB相关权重中选择的关键机制来解决每个任务。通过使用Mark，我们实现了最普遍的基准，在几个流行的基准中实现了最新的基准，在20分拆性MiniimAgenet数据集上超过了平均精度的最佳性能方法，同时使用55％的数量来实现几乎零遗忘参数。此外，消融研究提供了证据，实际上，标记正在学习每个任务选择性地使用的可重复使用的知识。

This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying a transformation to a selected subset of variables from the current solution. Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space. To learn a good variable selection strategy, we formulate the problem as a classification task that exploits structural information from the characteristics of the problem and from high-quality solutions. We validate our methodology on two metaheuristic applications: a Tabu Search scheme for solving a Wireless Network Optimization problem and a Large Neighborhood Search heuristic for solving Mixed-Integer Programs. The experimental results show that our approach is able to achieve a satisfactory trade-off between the exploration of a larger solution space and the exploitation of high-quality solution regions on both applications.

We investigate the asymptotic properties of deep Residual networks (ResNets) as the number of layers increases. We first show the existence of scaling regimes for trained weights markedly different from those implicitly assumed in the neural ODE literature. We study the convergence of the hidden state dynamics in these scaling regimes, showing that one may obtain an ODE, a stochastic differential equation (SDE) or neither of these. In particular, our findings point to the existence of a diffusive regime in which the deep network limit is described by a class of stochastic differential equations (SDEs). Finally, we derive the corresponding scaling limits for the backpropagation dynamics.

Warning: this paper contains content that may be offensive or upsetting. Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderators in restricting the spread of offensive language, abuse, and hate speech. Despite the success of large language models and the spontaneous emergence of slang dictionaries, it is unclear how far their combination goes in terms of slang understanding for downstream social good tasks. In this paper, we provide a framework to study different combinations of representation learning models and knowledge resources for a variety of downstream tasks that rely on slang understanding. Our experiments show the superiority of models that have been pre-trained on social media data, while the impact of dictionaries is positive only for static word embeddings. Our error analysis identifies core challenges for slang representation learning, including out-of-vocabulary words, polysemy, variance, and annotation disagreements, which can be traced to characteristics of slang as a quickly evolving and highly subjective language.

Warning: this paper contains content that may be offensive or upsetting. In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consider the semantics of the memes or the context of their creation. In this paper, we pursue a modular and explainable architecture for Internet meme understanding. We design and implement multimodal classification methods that perform example- and prototype-based reasoning over training cases, while leveraging both textual and visual SOTA models to represent the individual cases. We study the relevance of our modular and explainable models in detecting harmful memes on two existing tasks: Hate Speech Detection and Misogyny Classification. We compare the performance between example- and prototype-based methods, and between text, vision, and multimodal models, across different categories of harmfulness (e.g., stereotype and objectification). We devise a user-friendly interface that facilitates the comparative analysis of examples retrieved by all of our models for any given meme, informing the community about the strengths and limitations of these explainable methods.

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. In this work, we propose to capture each of these aspects by modeling the surgical scene with a disentangled latent scene graph representation, which we can then process using a graph neural network. Unlike previous approaches using graph representations, we explicitly encode in our graphs semantic information such as object locations and shapes, class probabilities and visual features. We also incorporate an auxiliary image reconstruction objective to help train the latent graph representations. We demonstrate the value of these components through comprehensive ablation studies and achieve state-of-the-art results for critical view of safety prediction across multiple experimental settings.

Reinforcement Learning has emerged as a strong alternative to solve optimization tasks efficiently. The use of these algorithms highly depends on the feedback signals provided by the environment in charge of informing about how good (or bad) the decisions made by the learned agent are. Unfortunately, in a broad range of problems the design of a good reward function is not trivial, so in such cases sparse reward signals are instead adopted. The lack of a dense reward function poses new challenges, mostly related to exploration. Imitation Learning has addressed those problems by leveraging demonstrations from experts. In the absence of an expert (and its subsequent demonstrations), an option is to prioritize well-suited exploration experiences collected by the agent in order to bootstrap its learning process with good exploration behaviors. However, this solution highly depends on the ability of the agent to discover such trajectories in the early stages of its learning process. To tackle this issue, we propose to combine imitation learning with intrinsic motivation, two of the most widely adopted techniques to address problems with sparse reward. In this work intrinsic motivation is used to encourage the agent to explore the environment based on its curiosity, whereas imitation learning allows repeating the most promising experiences to accelerate the learning process. This combination is shown to yield an improved performance and better generalization in procedurally-generated environments, outperforming previously reported self-imitation learning methods and achieving equal or better sample efficiency with respect to intrinsic motivation in isolation.

Because of the considerable heterogeneity and complexity of the technological landscape, building accurate models to forecast is a challenging endeavor. Due to their high prevalence in many complex systems, S-curves are a popular forecasting approach in previous work. However, their forecasting performance has not been directly compared to other technology forecasting approaches. Additionally, recent developments in time series forecasting that claim to improve forecasting accuracy are yet to be applied to technological development data. This work addresses both research gaps by comparing the forecasting performance of S-curves to a baseline and by developing an autencoder approach that employs recent advances in machine learning and time series forecasting. S-curves forecasts largely exhibit a mean average percentage error (MAPE) comparable to a simple ARIMA baseline. However, for a minority of emerging technologies, the MAPE increases by two magnitudes. Our autoencoder approach improves the MAPE by 13.5% on average over the second-best result. It forecasts established technologies with the same accuracy as the other approaches. However, it is especially strong at forecasting emerging technologies with a mean MAPE 18% lower than the next best result. Our results imply that a simple ARIMA model is preferable over the S-curve for technology forecasting. Practitioners looking for more accurate forecasts should opt for the presented autoencoder approach.