Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the "Update by Disagreement" strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Co-teaching+ is much superior to many state-of-the-art methods in the robustness of trained models.

Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not well understood. In this paper, we seek a crisp, clean and precise characterization of their behavior in nonconvex settings. To this end, we first provide a novel view of adaptive methods as preconditioned SGD, where the preconditioner is estimated in an online manner. By studying the preconditioner on its own, we elucidate its purpose: it rescales the stochastic gradient noise to be isotropic near stationary points, which helps escape saddle points. Furthermore, we show that adaptive methods can efficiently estimate the aforementioned preconditioner. By gluing together these two components, we provide the first (to our knowledge) second-order convergence result for any adaptive method. The key insight from our analysis is that, compared to SGD, adaptive methods escape saddle points faster, and can converge faster overall to second-order stationary points.

In this work, we introduce a novel framework that employs cluster annotation to boost active learning by reducing the number of human interactions required to train deep neural networks. Instead of annotating single samples individually, humans can also label clusters, producing a higher number of annotated samples with the cost of a small label error. Our experiments show that the proposed framework requires 82% and 87% less human interactions for CIFAR-10 and EuroSAT datasets respectively when compared with the fully-supervised training while maintaining similar performance on the test set.

Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amountof time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering basedheuristic approach called Heuristic Kalman Algorithm (HKA) has been proposed a few years ago, which may be used for optimizing an objective functionin data/feature space. In this paper at first HKA is employed in partitional data clustering. Then an improved approach named HKA-K is proposed, whichcombines the benefits of global exploration of HKA and the fast convergence of K-Means method. Implemented and tested on several datasets from UCI machine learning repository, the results obtained by HKA-K were compared with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K is atleast as good as and often better than the other compared algorithms. Keywords:Clustering, K-Means, Optimization, Metaheuristic Optimization, Heuristics 1. Introduction Clustering is the process of assigning a set of n data points into C classes based on the similarity between the data points in the feature space. It is useful when some prototype data from known classes are not available for training a supervised classifier or for an exploratory data analysis task. It is one of the earliest pattern classification approaches and has found renewed interest since the beginning of data mining and big data analytics.

Two things seem to be indisputable in the contemporary deep learning discourse: 1. The categorical cross-entropy loss after softmax activation is the method of choice for classification. 2. Training a CNN classifier from scratch on small datasets does not work well. In contrast to this, we show that the cosine loss function provides significantly better performance than cross-entropy on datasets with only a handful of samples per class. For example, the accuracy achieved on the CUB-200-2011 dataset without pre-training is by 30% higher than with the cross-entropy loss. Further experiments on four other popular datasets confirm our findings. Moreover, we show that the classification performance can be improved further by integrating prior knowledge in the form of class hierarchies, which is straightforward with the cosine loss.

The two state-of-the-art implementations of boosted trees: XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing boosted trees. which achieves a significant speedup over XGBoost and LightGBM, especially when memory size is small. This is achieved using a combination of two techniques: early stopping and stratified sampling, which are explained and analyzed in the paper. We describe our implementation and present experimental results to support our claims.

Recurrent neural networks are a widely used class of neural architectures. They have, however, two shortcomings. First, it is difficult to understand what exactly they learn. Second, they tend to work poorly on sequences requiring long-term memorization, despite having this capacity in principle. We aim to address both shortcomings with a class of recurrent networks that use a stochastic state transition mechanism between cell applications. This mechanism, which we term state-regularization, makes RNNs transition between a finite set of learnable states. We evaluate state-regularized RNNs on (1) regular languages for the purpose of automata extraction; (2) nonregular languages such as balanced parentheses, palindromes, and the copy task where external memory is required; and (3) real-word sequence learning tasks for sentiment analysis, visual object recognition, and language modeling. We show that state-regularization (a) simplifies the extraction of finite state automata modeling an RNN's state transition dynamics; (b) forces RNNs to operate more like automata with external memory and less like finite state machines; (c) makes RNNs have better interpretability and explainability.

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Artificial intelligence within CRM still has to make inroads with the pharmaceutical industry. That's a surprising conclusion given how much this sector is embracing AI for other functions. Yet some market analysts are calling this a "white space" for the industry. Pharma companies are already incorporating AI in their drug delivery, digital diagnostics and precision medicine programs -- areas where its usage has become highly sophisticated. The industry clearly recognizes that AI can be a massive value add.

Artificial intelligence (AI) presents limitless opportunity, but not without potential pitfalls and risks. This paradox has become increasingly evident for government leaders. They want to give domestic companies an edge over the competition, but are also expected to protect their citizens and use AI for social good. They want to support innovation, while still maintaining some level of control over how new technologies impact society at large. With a huge payoff on the line -- by our own estimates, AI has the potential to increase worldwide GDP by 14 percent by 2030, an infusion of US$15.7 trillion into the global economy -- it should come as no surprise that governments are eager to claim their share.