稀疏卷积神经网络（CNNS）在过去几年中获得了显着的牵引力，因为与其致密的对应物相比，稀疏的CNNS可以大大降低模型尺寸和计算。稀疏的CNN经常引入层形状和尺寸的变化，这可以防止密集的加速器在稀疏的CNN模型上执行良好。最近提出的稀疏加速器，如SCNN，Eyeriss V2和Sparten，积极利用双面或全稀稀物质，即重量和激活的稀疏性，用于性能收益。然而，这些加速器具有低效的微架构，其限制了它们的性能，而不对非单位步幅卷积和完全连接（Fc）层的支持，或者遭受系统负荷不平衡的大规模遭受。为了规避这些问题并支持稀疏和密集的模型，我们提出了幻影，多线程，动态和灵活的神经计算核心。 Phantom使用稀疏二进制掩码表示，以主动寻求稀疏计算，并动态调度其计算线程以最大化线程利用率和吞吐量。我们还生成了幻象神经计算核心的二维（2D）网格体系结构，我们将其称为Phantom-2D加速器，并提出了一种支持CNN的所有层的新型数据流，包括单位和非单位步幅卷积，和fc层。此外，Phantom-2D使用双级负载平衡策略来最小化计算空闲，从而进一步提高硬件利用率。为了向不同类型的图层显示支持，我们评估VGG16和MobileNet上的幻影架构的性能。我们的模拟表明，Phantom-2D加速器分别达到了12倍，4.1 X，1.98x和2.36倍，超密架构，SCNN，Sparten和Eyeriss V2的性能增益。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The physical duration between one decision and the next becomes a critical hyperparameter. When this duration is too short, the agent needs to make many decisions to achieve its goal, aggravating the problem's difficulty. But when this duration is too long, the agent becomes incapable of controlling the system. Physical systems, however, do not need a constant control frequency. For learning agents, it is desirable to operate with low frequency when possible and high frequency when necessary. We propose a framework called Continuous-Time Continuous-Options (CTCO), where the agent chooses options as sub-policies of variable durations. Such options are time-continuous and can interact with the system at any desired frequency providing a smooth change of actions. The empirical analysis shows that our algorithm is competitive w.r.t. other time-abstraction techniques, such as classic option learning and action repetition, and practically overcomes the difficult choice of the decision frequency.

Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: ~\url{https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data}.

Medical professionals frequently work in a data constrained setting to provide insights across a unique demographic. A few medical observations, for instance, informs the diagnosis and treatment of a patient. This suggests a unique setting for meta-learning, a method to learn models quickly on new tasks, to provide insights unattainable by other methods. We investigate the use of meta-learning and robustness techniques on a broad corpus of benchmark text and medical data. To do this, we developed new data pipelines, combined language models with meta-learning approaches, and extended existing meta-learning algorithms to minimize worst case loss. We find that meta-learning on text is a suitable framework for text-based data, providing better data efficiency and comparable performance to few-shot language models and can be successfully applied to medical note data. Furthermore, meta-learning models coupled with DRO can improve worst case loss across disease codes.

Data scarcity is a notable problem, especially in the medical domain, due to patient data laws. Therefore, efficient Pre-Training techniques could help in combating this problem. In this paper, we demonstrate that a model trained on the time direction of functional neuro-imaging data could help in any downstream task, for example, classifying diseases from healthy controls in fMRI data. We train a Deep Neural Network on Independent components derived from fMRI data using the Independent component analysis (ICA) technique. It learns time direction in the ICA-based data. This pre-trained model is further trained to classify brain disorders in different datasets. Through various experiments, we have shown that learning time direction helps a model learn some causal relation in fMRI data that helps in faster convergence, and consequently, the model generalizes well in downstream classification tasks even with fewer data records.

Learning long-horizon tasks such as navigation has presented difficult challenges for successfully applying reinforcement learning. However, from another perspective, under a known environment model, methods such as sampling-based planning can robustly find collision-free paths in environments without learning. In this work, we propose Control Transformer which models return-conditioned sequences from low-level policies guided by a sampling-based Probabilistic Roadmap (PRM) planner. Once trained, we demonstrate that our framework can solve long-horizon navigation tasks using only local information. We evaluate our approach on partially-observed maze navigation with MuJoCo robots, including Ant, Point, and Humanoid, and show that Control Transformer can successfully navigate large mazes and generalize to new, unknown environments. Additionally, we apply our method to a differential drive robot (Turtlebot3) and show zero-shot sim2real transfer under noisy observations.

The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This paper proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on an expert curated data set which demonstrate the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).

智能仪表测量值虽然对于准确的需求预测至关重要，但仍面临一些缺点，包括消费者的隐私，数据泄露问题，仅举几例。最近的文献探索了联合学习（FL）作为一种有前途的隐私机器学习替代方案，该替代方案可以协作学习模型，而无需将私人原始数据暴露于短期负载预测中。尽管有着美德，但标准FL仍然容易受到棘手的网络威胁，称为拜占庭式攻击，这是由错误和/或恶意客户进行的。因此，为了提高联邦联邦短期负载预测对拜占庭威胁的鲁棒性，我们开发了一个最先进的基于私人安全的FL框架，以确保单个智能电表的数据的隐私，同时保护FL的安全性模型和架构。我们提出的框架利用了通过符号随机梯度下降（SignsGD）算法的梯度量化的想法，在本地模型培训后，客户仅将梯度的“符号”传输到控制中心。当我们通过涉及一组拜占庭攻击模型的基准神经网络的实验突出显示时，我们提出的方法会非常有效地减轻此类威胁，从而优于常规的FED-SGD模型。

身份验证系统容易受到模型反演攻击的影响，在这种攻击中，对手能够近似目标机器学习模型的倒数。生物识别模型是这种攻击的主要候选者。这是因为反相生物特征模型允许攻击者产生逼真的生物识别输入，以使生物识别认证系统欺骗。进行成功模型反转攻击的主要限制之一是所需的训练数据量。在这项工作中，我们专注于虹膜和面部生物识别系统，并提出了一种新技术，可大大减少必要的训练数据量。通过利用多个模型的输出，我们能够使用1/10进行模型反演攻击，以艾哈迈德和富勒（IJCB 2020）的训练集大小（IJCB 2020）进行虹膜数据，而Mai等人的训练集大小为1/1000。 （模式分析和机器智能2019）的面部数据。我们将新的攻击技术表示为结构性随机，并损失对齐。我们的攻击是黑框，不需要了解目标神经网络的权重，只需要输出向量的维度和值。为了显示对齐损失的多功能性，我们将攻击框架应用于会员推理的任务（Shokri等，IEEE S＆P 2017），对生物识别数据。对于IRIS，针对分类网络的会员推断攻击从52％提高到62％的准确性。