Binary classification is a fundamental task in machine learning, with applications spanning various scientific domains. Whether scientists are conducting fundamental research or refining practical applications, they typically assess and rank classification techniques based on performance metrics such as accuracy, sensitivity, and specificity. However, reported performance scores may not always serve as a reliable basis for research ranking. This can be attributed to undisclosed or unconventional practices related to cross-validation, typographical errors, and other factors. In a given experimental setup, with a specific number of positive and negative test items, most performance scores can assume specific, interrelated values. In this paper, we introduce numerical techniques to assess the consistency of reported performance scores and the assumed experimental setup. Importantly, the proposed approach does not rely on statistical inference but uses numerical methods to identify inconsistencies with certainty. Through three different applications related to medicine, we demonstrate how the proposed techniques can effectively detect inconsistencies, thereby safeguarding the integrity of research fields. To benefit the scientific community, we have made the consistency tests available in an open-source Python package.
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Graph Neural Networks (GNNs) have displayed considerable promise in graph representation learning across various applications. The core learning process requires the initialization of model weight matrices within each GNN layer, which is typically accomplished via classic initialization methods such as Xavier initialization. However, these methods were originally motivated to stabilize the variance of hidden embeddings and gradients across layers of Feedforward Neural Networks (FNNs) and Convolutional Neural Networks (CNNs) to avoid vanishing gradients and maintain steady information flow. In contrast, within the GNN context classical initializations disregard the impact of the input graph structure and message passing on variance. In this paper, we analyze the variance of forward and backward propagation across GNN layers and show that the variance instability of GNN initializations comes from the combined effect of the activation function, hidden dimension, graph structure and message passing. To better account for these influence factors, we propose a new initialization method for Variance Instability Reduction within GNN Optimization (Virgo), which naturally tends to equate forward and backward variances across successive layers. We conduct comprehensive experiments on 15 datasets to show that Virgo can lead to superior model performance and more stable variance at initialization on node classification, link prediction and graph classification tasks. Codes are in //github.com/LspongebobJH/virgo_icml2023.
DP-SGD has emerged as a popular method to protect personally identifiable information in deep learning applications. Unfortunately, DP-SGD's per-sample gradient clipping and uniform noise addition during training can significantly degrade model utility. To enhance the model's utility, researchers proposed various adaptive DP-SGD methods. However, we examine and discover that these techniques result in greater privacy leakage or lower accuracy than the traditional DP-SGD method, or a lack of evaluation on a complex data set such as CIFAR100. To address these limitations, we propose an Auto DP-SGD. Our method automates clipping threshold estimation based on the DL model's gradient norm and scales the gradients of each training sample without losing gradient information. This helps to improve the algorithm's utility while using a less privacy budget. To further improve accuracy, we introduce automatic noise multiplier decay mechanisms to decrease the noise multiplier after every epoch. Finally, we develop closed-form mathematical expressions using tCDP accountant for automatic noise multiplier and automatic clipping threshold estimation. Through extensive experimentation, we demonstrate that Auto DP-SGD outperforms existing SOTA DP-SGD methods in privacy and accuracy on various benchmark datasets. We also show that privacy can be improved by lowering the scale factor and using learning rate schedulers without significantly reducing accuracy. Specifically, Auto DP-SGD, when used with a step noise multiplier, improves accuracy by 3.20, 1.57, 6.73, and 1.42 for the MNIST, CIFAR10, CIFAR100, and AG News Corpus datasets, respectively. Furthermore, it obtains a substantial reduction in the privacy budget of 94.9, 79.16, 67.36, and 53.37 for the corresponding data sets.
The evaluation of abstractive summarization models typically uses test data that is identically distributed as training data. In real-world practice, documents to be summarized may contain input noise caused by text extraction artifacts or data pipeline bugs. The robustness of model performance under distribution shift caused by such noise is relatively under-studied. We present a large empirical study quantifying the sometimes severe loss in performance (up to 12 ROUGE-1 points) from different types of input noise for a range of datasets and model sizes. We then propose a light-weight method for detecting and removing such noise in the input during model inference without requiring any extra training, auxiliary models, or even prior knowledge of the type of noise. Our proposed approach effectively mitigates the loss in performance, recovering a large fraction of the performance drop, sometimes as large as 11 ROUGE-1 points.
Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can be seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer. By stacking two such layers and feeding the output of one layer to the next, we obtain a Deep Language Network (DLN). We first show how to effectively perform prompt optimization for a 1-Layer language network (DLN-1). Then, we present an extension that applies to 2-layer DLNs (DLN-2), where two prompts must be learned. The key idea is to consider the output of the first layer as a latent variable, which requires inference, and prompts to be learned as the parameters of the generative distribution. We first test the effectiveness of DLN-1 in multiple reasoning and natural language understanding tasks. Then, we show that DLN-2 can reach higher performance than a single layer, showing promise that we might reach comparable performance to GPT-4, even when each LLM in the network is smaller and less powerful.
Software log analysis can be laborious and time consuming. Time and labeled data are usually lacking in industrial settings. This paper studies unsupervised and time efficient methods for anomaly detection. We study two custom and two established models. The custom models are: an OOV (Out-Of-Vocabulary) detector, which counts the terms in the test data that are not present in the training data, and the Rarity Model (RM), which calculates a rarity score for terms based on their infrequency. The established models are KMeans and Isolation Forest. The models are evaluated on four public datasets (BGL, Thunderbird, Hadoop, HDFS) with three different representation techniques for the log messages (Words, character Trigrams, Parsed events). We used the AUC-ROC metric for the evaluation. The results reveal discrepancies based on the dataset and representation technique. Different configurations are advised based on specific requirements. For speed, the OOV detector with word representation is optimal. For accuracy, the OOV detector combined with trigram representation yields the highest AUC-ROC (0.846). When dealing with unfiltered data where training includes both normal and anomalous instances, the most effective combination is the Isolation Forest with event representation, achieving an AUC-ROC of 0.829.
Adversarial training is an important topic in robust deep learning, but the community lacks attention to its practical usage. In this paper, we aim to resolve a real-world challenge, i.e., training a model on an imbalanced and noisy dataset to achieve high clean accuracy and adversarial robustness, with our proposed Omnipotent Adversarial Training (OAT) strategy. OAT consists of two innovative methodologies to address the imperfection in the training set. We first introduce an oracle into the adversarial training process to help the model learn a correct data-label conditional distribution. This carefully-designed oracle can provide correct label annotations for adversarial training. We further propose logits adjustment adversarial training to overcome the data imbalance issue, which can help the model learn a Bayes-optimal distribution. Our comprehensive evaluation results show that OAT outperforms other baselines by more than 20% clean accuracy improvement and 10% robust accuracy improvement under complex combinations of data imbalance and label noise scenarios. The code can be found in //github.com/GuanlinLee/OAT.
Testing complex simulation models can be expensive and time consuming. Current state-of-the-art methods that explore this problem are fully-supervised; i.e. they require that all examples are labeled. On the other hand, the GenClu system (introduced in this paper) takes a semi-supervised approach; i.e. (a) only a small subset of information is actually labeled (via simulation) and (b) those labels are then spread across the rest of the data. When applied to five open-source simulation models of cyber-physical systems, GenClu's test generation can be multiple orders of magnitude faster than the prior state of the art. Further, when assessed via mutation testing, tests generated by GenClu were as good or better than anything else tested here. Hence, we recommend semi-supervised methods over prior methods (evolutionary search and fully-supervised learning).
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing concern in the current era. This paper provides a comprehensive review of recent advances in aligning LLMs with the ever-changing world knowledge without re-training from scratch. We categorize research works systemically and provide in-depth comparisons and discussion. We also discuss existing challenges and highlight future directions to facilitate research in this field. We release the paper list at //github.com/hyintell/awesome-refreshing-llms
Object detection is a fundamental task in computer vision and image processing. Current deep learning based object detectors have been highly successful with abundant labeled data. But in real life, it is not guaranteed that each object category has enough labeled samples for training. These large object detectors are easy to overfit when the training data is limited. Therefore, it is necessary to introduce few-shot learning and zero-shot learning into object detection, which can be named low-shot object detection together. Low-Shot Object Detection (LSOD) aims to detect objects from a few or even zero labeled data, which can be categorized into few-shot object detection (FSOD) and zero-shot object detection (ZSD), respectively. This paper conducts a comprehensive survey for deep learning based FSOD and ZSD. First, this survey classifies methods for FSOD and ZSD into different categories and discusses the pros and cons of them. Second, this survey reviews dataset settings and evaluation metrics for FSOD and ZSD, then analyzes the performance of different methods on these benchmarks. Finally, this survey discusses future challenges and promising directions for FSOD and ZSD.
Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.
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