Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging. Using Machine Learning (ML) for this problem generally requires manually annotated ground-truth segmentations, demanding extensive time and resources from radiologists. This work presents a novel weakly supervised approach that utilizes binary image-level labels, which are much simpler to acquire, to effectively segment anomalies in medical Magnetic Resonance (MR) images without ground truth annotations. We train a binary classifier using these labels and use it to derive seeds indicating regions likely and unlikely to contain tumors. These seeds are used to train a generative adversarial network (GAN) that converts cancerous images to healthy variants, which are then used in conjunction with the seeds to train a ML model that generates effective segmentations. This method produces segmentations that achieve Dice coefficients of 0.7903, 0.7868, and 0.7712 on the MICCAI Brain Tumor Segmentation (BraTS) 2020 dataset for the training, validation, and test cohorts respectively. We also propose a weakly supervised means of filtering the segmentations, removing a small subset of poorer segmentations to acquire a large subset of high quality segmentations. The proposed filtering further improves the Dice coefficients to up to 0.8374, 0.8232, and 0.8136 for training, validation, and test, respectively.

脑肿瘤分割是肿瘤体积分析和AI算法的关键任务。然而，它是一种耗时的过程，需要神经加理学专业知识。虽然已经进行了广泛的研究，其专注于在成人人群中优化脑肿瘤细分，但对AI引导的儿科肿瘤细分的研究是稀缺的。此外，儿科和成人脑肿瘤的MRI信号特征不同，需要开发专为儿科脑肿瘤设计的分段算法。我们开发了一种在医院医院（Toronto，Ontario，加拿大）的磁共振成像（PLGGS）的磁共振成像（MRI）培训的分割模型。所提出的模型通过将肿瘤的遗传改变分类器添加为主网络的辅助任务来利用深度多任务学习（DMTL），最终提高分段结果的准确性。

Multilingual machine translation models can benefit from synergy between different language pairs, but also suffer from interference. While there is a growing number of sophisticated methods that aim to eliminate interference, our understanding of interference as a phenomenon is still limited. This work identifies the main factors that contribute to interference in multilingual machine translation. Through systematic experimentation, we find that interference (or synergy) are primarily determined by model size, data size, and the proportion of each language pair within the total dataset. We observe that substantial interference occurs mainly when the model is very small with respect to the available training data, and that using standard transformer configurations with less than one billion parameters largely alleviates interference and promotes synergy. Moreover, we show that tuning the sampling temperature to control the proportion of each language pair in the data is key to balancing the amount of interference between low and high resource language pairs effectively, and can lead to superior performance overall.

We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to the more general setting of density estimation (with respect to the total variation metric). The existence of a universally consistent DP learner reveals a stark difference with the distribution-free PAC model. Indeed, in the latter DP learning is extremely limited: even one-dimensional linear classifiers are not privately learnable in this stringent model. Our result thus demonstrates that by allowing the learning rate to depend on the target distribution, one can circumvent the above-mentioned impossibility result and in fact, learn \emph{arbitrary} distributions by a single DP algorithm. As an application, we prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal \emph{labeled} sample complexity of $\tilde{O}(d/\varepsilon)$ labeled examples (and with an unlabeled sample complexity that can depend on the target distribution).

Learned classifiers should often possess certain invariance properties meant to encourage fairness, robustness, or out-of-distribution generalization. However, multiple recent works empirically demonstrate that common invariance-inducing regularizers are ineffective in the over-parameterized regime, in which classifiers perfectly fit (i.e. interpolate) the training data. This suggests that the phenomenon of ``benign overfitting," in which models generalize well despite interpolating, might not favorably extend to settings in which robustness or fairness are desirable. In this work we provide a theoretical justification for these observations. We prove that -- even in the simplest of settings -- any interpolating learning rule (with arbitrarily small margin) will not satisfy these invariance properties. We then propose and analyze an algorithm that -- in the same setting -- successfully learns a non-interpolating classifier that is provably invariant. We validate our theoretical observations on simulated data and the Waterbirds dataset.

The problem of learning threshold functions is a fundamental one in machine learning. Classical learning theory implies sample complexity of $O(\xi^{-1} \log(1/\beta))$ (for generalization error $\xi$ with confidence $1-\beta$). The private version of the problem, however, is more challenging and in particular, the sample complexity must depend on the size $|X|$ of the domain. Progress on quantifying this dependence, via lower and upper bounds, was made in a line of works over the past decade. In this paper, we finally close the gap for approximate-DP and provide a nearly tight upper bound of $\tilde{O}(\log^* |X|)$, which matches a lower bound by Alon et al (that applies even with improper learning) and improves over a prior upper bound of $\tilde{O}((\log^* |X|)^{1.5})$ by Kaplan et al. We also provide matching upper and lower bounds of $\tilde{\Theta}(2^{\log^*|X|})$ for the additive error of private quasi-concave optimization (a related and more general problem). Our improvement is achieved via the novel Reorder-Slice-Compute paradigm for private data analysis which we believe will have further applications.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event -- or a reasoning-graph. To employ large language models (LMs) for this task, existing approaches ``serialize'' the output graph as a flat list of nodes and edges. Although feasible, these serialized graphs strongly deviate from the natural language corpora that LMs were pre-trained on, hindering LMs from generating them correctly. In this paper, we show that when we instead frame structured commonsense reasoning tasks as code generation tasks, pre-trained LMs of code are better structured commonsense reasoners than LMs of natural language, even when the downstream task does not involve source code at all. We demonstrate our approach across three diverse structured commonsense reasoning tasks. In all these natural language tasks, we show that using our approach, a code generation LM (CODEX) outperforms natural-LMs that are fine-tuned on the target task (e.g., T5) and other strong LMs such as GPT-3 in the few-shot setting.

近年来，多级预测广受欢迎。因此，衡量拟合度的善良成为研究人员经常必须处理的基本问题。几个指标通常用于此任务。但是，当人们必须决定正确的测量值时，他必须考虑不同的用例施加了控制这一决定的不同约束。至少在\ emph {现实世界}多级问题中的主要约束是不平衡的数据：多类问题几乎无法提供对称数据。因此，当我们观察到常见的KPI（关键性能指标）时，例如精度敏感性或准确性时，很少会将所获得的数字解释为模型的实际需求。我们建议将Matthew的相关系数概括为多维。该概括基于对广义混淆矩阵的几何解释。

正如最近的作品中观察到的那样，通信图神经网络（GNN）中信号传播的质量强烈影响其表现力。特别是，对于依靠远程相互作用的预测任务，节点特征的递归聚合可能导致不希望的现象称为“过句”。我们提出了一个基于信息收缩的分析过度句子的框架。我们的分析以可靠计算的模型为指导，该模型由于冯·诺伊曼（Von Neumann），该模型在嘈杂的计算图中提供了新的洞察力作为信号淬灭的新见解。在此基础上，我们提出了一个旨在减轻过度量化的算法的图形。我们的算法采用了由扩展器图构造动机的随机局部边缘翻转原始的。我们将算法的光谱膨胀特性与现有基于曲率的非本地重新布线策略的光谱膨胀属性进行了比较。合成实验表明，尽管我们的算法通常具有较慢的膨胀速率，但总体计算更便宜，可以准确地保留节点度，并且永远不会断开图表的连接。