Few-shot learning algorithms aim to learn model parameters capable of adapting unseen classes with the help only a few labeled examples. A recent regularization technique - Manifold Mixup focuses on general-purpose representation, robust small changes in data distribution. Since goal few-shot is closely linked representation learning, we study this problem setting. Self-supervised another that learns semantically meaningful features, using inherent structure data. This work investigates role...

Auto-bidding problem under a strict return-on-spend constraint (ROSC) is considered, where an algorithm has to make decisions about how much bid for ad slot depending on the revealed value, and hidden allocation payment function that describes probability of winning ad-slot its bid. The objective maximize expected utility (product value slot) summed across all time slots subject total being less than utility, called ROSC. A (surprising) impossibility result derived shows no online can...

The Ganga Plain (GP) is one of the largest alluvial floodplains in world, where river meanders and floodplain wetlands are crucial surface water resources that support millions people. Nearly 80% GP geographically isolated (GIWs), meaning precipitation provides most their water. These GIWs shrink develop into vegetative patches as a result groundwater extraction surrounding regions. This study has utilized geostatistical approaches to analyse hydrological connectivity rainfall patterns for...

Protein sequence analysis of the cloned sialyltransferase gene family has revealed presence two conserved protein motifs in middle lumenal catalytic domain, termed L-sialylmotif and S-sialylmotif. In our previous study (Datta, A. K., Paulson, J. C. (1995) Biol. Chem.270, 1497–1500) larger ST6Gal I was analyzed by site-directed mutagenesis, which provided evidence that it participates binding CMP-NeuAc, a common donor substrate for all sialyltransferases. However, none mutants tested this...

Power grids are one of the most important components infrastructure in today's world. Every nation is dependent on security and stability its own power grid to provide electricity households industries. A malfunction even a small part can cause loss productivity, revenue some cases life. Thus, it imperative design system which detect health take protective measures accordingly before serious anomaly takes place. To achieve this objective, we have set out create an artificially intelligent...

Neural networks are vulnerable to adversarial attacks - small visually imperceptible crafted noise which when added the input drastically changes output. The most effective method of defending against is use methodology training. We analyze adversarially trained robust models study their vulnerability at level latent layers. Our analysis reveals that contrary layer attack, these highly susceptible perturbations magnitude. Leveraging this information, we introduce a new technique Latent...

Deep neural networks have shown promising results in image super-resolution by learning a complex mapping from low resolution to high image. However, most of the approaches learns upsample using convolution spatial domain and are confined local features. This into restricting receptive field network therefore deteriorates overall quality high-resolution To alleviate this issue, we propose an architecture that both global features, fuses them together generate images. The uses non-local...

Grinding is a process of reduction lumps to powder depending on the requirement particle size and shape. The present investigation involves identify physical properties three different types iron ores by using Bond ball mill. result shows that maximum work index 14 KWh/mt was obtained for 'A' type ore sample. 'B' 'C' sample are 11 10 KWh/mt. variation BWI may be varied during geological formation each output product composed fractions when ground in Bonds Based samples classified as hard...

Visual content based product retrieval has become increasingly important for e-commerce. Fashion retrieval, in particular, is a challenging problem owing to wide range of deformations clothing items along with visual distortions their images. In this paper, we propose Grid Search Network (GSN) learning feature embeddings fashion retrieval. The proposed approach posits the training procedure as search problem, focused on locating matches reference query image grid containing both positive and...

In this evolving era of machine learning security, membership inference attacks have emerged as a potent threat to the confidentiality sensitive data. attack, adversaries aim determine whether particular point was used during training target model. This paper proposes new method gauge data point’s in model’s set. Instead correlating loss with membership, is traditionally done, we leveraged fact that examples generally exhibit higher confidence values when classified into their actual class....

Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to can be expensive acquire. This paper describes Diffusion-Decoding with Contrastive representations (D2C), a paradigm for training unconditional variational autoencoders (VAEs) few-shot conditional image generation. D2C uses learned diffusion-based prior over the latent improve generation and contrastive self-supervised learning representation quality. adapt novel tasks...

In this work, we focus on the problem of grounding language by training an agent to follow a set natural instructions and navigate target object in environment. The receives visual information through raw pixels instruction telling what task needs be achieved is trained end-to-end way. We develop attention mechanism for multi-modal fusion textual modalities that allows learn complete achieve grounding. Our experimental results show our outperforms existing mechanisms proposed both 2D 3D...