Purpose: Image classification may be the fundamental task in imaging artificial intelligence. We have recently shown that reinforcement learning can achieve high accuracy for lesion localization and segmentation even with minuscule training sets. Here, we introduce reinforcement learning for image classification. In particular, we apply the approach to normal vs. tumor-containing 2D MRI brain images. Materials and Methods: We applied multi-step image classification to allow for combined Deep Q learning and TD(0) Q learning. We trained on a set of 30 images (15 normal and 15 tumor-containing). We tested on a separate set of 30 images (15 normal and 15 tumor-containing). For comparison, we also trained and tested a supervised deep-learning classification network on the same set of training and testing images. Results: Whereas the supervised approach quickly overfit the training data and as expected performed poorly on the testing set (57% accuracy, just over random guessing), the reinforcement learning approach achieved an accuracy of 100%. Conclusion: We have shown a proof-of-principle application of reinforcement learning to the classification of brain tumors. We achieved perfect testing set accuracy with a training set of merely 30 images.

Data-to-Text Generation (DTG) is a subfield of Natural Language Generation aiming at transcribing structured data in natural language descriptions. The field has been recently boosted by the use of neural-based generators which exhibit on one side great syntactic skills without the need of hand-crafted pipelines; on the other side, the quality of the generated text reflects the quality of the training data, which in realistic settings only offer imperfectly aligned structure-text pairs. Consequently, state-of-art neural models include misleading statements - usually called hallucinations - in their outputs. The control of this phenomenon is today a major challenge for DTG, and is the problem addressed in the paper. Previous work deal with this issue at the instance level: using an alignment score for each table-reference pair. In contrast, we propose a finer-grained approach, arguing that hallucinations should rather be treated at the word level. Specifically, we propose a Multi-Branch Decoder which is able to leverage word-level labels to learn the relevant parts of each training instance. These labels are obtained following a simple and efficient scoring procedure based on co-occurrence analysis and dependency parsing. Extensive evaluations, via automated metrics and human judgment on the standard WikiBio benchmark, show the accuracy of our alignment labels and the effectiveness of the proposed Multi-Branch Decoder. Our model is able to reduce and control hallucinations, while keeping fluency and coherence in generated texts. Further experiments on a degraded version of ToTTo show that our model could be successfully used on very noisy settings.

We describe the EFloat floating-point number format with 4 to 6 additional bits of precision and a wider exponent range than the existing floating point (FP) formats of any width including FP32, BFloat16, IEEE-Half precision, DLFloat, TensorFloat, and 8-bit floats. In a large class of deep learning models we observe that FP exponent values tend to cluster around few unique values which presents entropy encoding opportunities. The EFloat format encodes frequent exponent values and signs with Huffman codes to minimize the average exponent field width. Saved bits then become available to the mantissa increasing the EFloat numeric precision on average by 4 to 6 bits compared to other FP formats of equal width. The proposed encoding concept may be beneficial to low-precision formats including 8-bit floats. Training deep learning models with low precision arithmetic is challenging. EFloat, with its increased precision may provide an opportunity for those tasks as well. We currently use the EFloat format for compressing and saving memory used in large NLP deep learning models. A potential hardware implementation for improving PCIe and memory bandwidth limitations of AI accelerators is also discussed.

To detect fake news, researchers proposed to use The proliferation of biased news, misleading linguistics and textual content (Castillo et al., 2011; claims, disinformation and fake news has caused Zhao et al., 2015; Liu et al., 2015). Since textual heightened negative effects on modern society in claims are usually deliberately written to deceive various domains ranging from politics, economics readers, it is hard to detect fake news by solely to public health. A recent study showed that maliciously relying on the content claims. Therefore, multiple fabricated and partisan stories possibly works utilized other signals such as temporal caused citizens' misperception about political candidates spreading patterns (Liu and Wu, 2018), network (Allcott and Gentzkow, 2017) during the structures (Wu and Liu, 2018; Vo and Lee, 2018; 2016 U.S. presidential elections. In economics, the Shu et al., 2020) and users' feedbacks (Vo and spread of fake news has manipulated stock price Lee, 2019; Shu et al., 2019; Vo and Lee, 2020a).

Predicting (1) when the next hospital admission occurs and (2) what will happen in the next admission about a patient by mining electronic health record (EHR) data can provide granular readmission predictions to assist clinical decision making. Recurrent neural network (RNN) and point process models are usually employed in modelling temporal sequential data. Simple RNN models assume that sequences of hospital visits follow strict causal dependencies between consecutive visits. However, in the real-world, a patient may have multiple co-existing chronic medical conditions, i.e., multimorbidity, which results in a cascade of visits where a non-immediate historical visit can be most influential to the next visit. Although a point process (e.g., Hawkes process) is able to model a cascade temporal relationship, it strongly relies on a prior generative process assumption. We propose a novel model, MEDCAS, to address these challenges. MEDCAS combines the strengths of RNN-based models and point processes by integrating point processes in modelling visit types and time gaps into an attention-based sequence-to-sequence learning model, which is able to capture the temporal cascade relationships. To supplement the patients with short visit sequences, a structural modelling technique with graph-based methods is used to construct the markers of the point process in MEDCAS. Extensive experiments on three real-world EHR datasets have been performed and the results demonstrate that \texttt{MEDCAS} outperforms state-of-the-art models in both tasks.

Inference attacks against Machine Learning (ML) models allow adversaries to learn information about training data, model parameters, etc. While researchers have studied these attacks thoroughly, they have done so in isolation. We lack a comprehensive picture of the risks caused by the attacks, such as the different scenarios they can be applied to, the common factors that influence their performance, the relationship among them, or the effectiveness of defense techniques. In this paper, we fill this gap by presenting a first-of-its-kind holistic risk assessment of different inference attacks against machine learning models. We concentrate on four attacks - namely, membership inference, model inversion, attribute inference, and model stealing - and establish a threat model taxonomy. Our extensive experimental evaluation conducted over five model architectures and four datasets shows that the complexity of the training dataset plays an important role with respect to the attack's performance, while the effectiveness of model stealing and membership inference attacks are negatively correlated. We also show that defenses like DP-SGD and Knowledge Distillation can only hope to mitigate some of the inference attacks. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models, and equally serves as a benchmark tool for researchers and practitioners.

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI) and for neurite orientation dispersion and density imaging (DIFFnetNODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.

DALL·E[1] is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs. We've found that it has a diverse set of capabilities, including creating anthropomorphized versions of animals and objects, combining unrelated concepts in plausible ways, rendering text, and applying transformations to existing images. GPT-3 showed that language can be used to instruct a large neural network to perform a variety of text generation tasks. Image GPT showed that the same type of neural network can also be used to generate images with high fidelity. We extend these findings to show that manipulating visual concepts through language is now within reach.

The era of technology and innovations is rapidly transforming our lives today. The potential of such technologies is extending beyond our imagination. The advent of advanced technologies such as computer vision is contributing enormously across industries. Among several industries, the agriculture industry is one such sector that has started incorporating computer vision in their mode of operations. Agriculture is considered the economy-boosting sector that makes every nation stand out in the global market.

The popularity of cryptocurrencies skyrocketed in 2017 due to a few continuous months of an exponential development of their showcase capitalization. The costs peaked at more than $800 billion in January 2018. Although machine learning has been effective in predicting stock showcase costs through a share of diverse time series models. Its application in anticipating cryptocurrency costs has been very prohibitive. The reason behind this can be self-evident as the costs of cryptocurrencies depend on a parcel of variables like mechanical advance.