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Recordings of a one-year-old s life train an AI system to learn words | Technology

Research establishes a computational basis to study how children begin to speak, connecting what they see with the auditory stimuli they receive from adults

Islas balearesComunidad autonoma de catalunaAntonio rodrYork universityUniversity of barcelonaInstitute of neurosciencesNew york universityWai keen vongContrastive learning

Grounded language acquisition through the eyes and ears of a single child

Grounded language acquisition through the eyes and ears of a single child
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Ekeoma uzogaraContrastive learning

Contrastive Learning Augmented Graph Auto-Encoder by Shuaishuai Zu, Chuyu Wang et al

Graph embedding aims to embed the information of graph data into low-dimensional representation space. Prior methods generally suffer from an imbalance of preserving structural information and node features due to their pre-defined inductive biases, leading to unsatisfactory generalization performance. In order to preserve the maximal information, graph contrastive learning (GCL) has become a prominent technique for learning discriminative embeddings. However, in contrast with graph-level embeddings, existing GCL methods generally learn less discriminative node embeddings in a self-supervised way. In this paper, we ascribe above problem to two challenges: 1) graph data augmentations, which are designed for generating contrastive representations, hurt the original semantic information for nodes. 2) the nodes within the same cluster are selected as negative samples. To alleviate these challenges, we propose Contrastive Variational Graph Auto-Encoder (CVGAE). Specifically, we first propos

Contrastive variational graph auto encoderContrastive learningIstribution dependent regularizationRaph auto encoderRuncated triplet loss

Synthetic imagery sets new bar in AI training efficiency

MIT researchers have developed StableRep, an AI training method using synthetic images generated by text-to-image models, which surpasses traditional training on real images. The approach leverages multi-positive contrastive learning, promising more efficient, less biased, and resource-conscious machine learning development.

Phillip isolaHuiwen changGoogle deepmindDavid fleetDilip krishnanUniversity of torontoArtificial intelligence laboratoryLijie fanYonglong tian phdNeural information processing systemsMit csailContrastive learningSynthetic imagesStable diffusionLijie fanAi image generator

CAKT: Coupling contrastive learning with attention networks for interp by Shuaishuai Zu, Li Li et al

In intelligent systems, knowledge tracing (KT) plays a vital role in providing personalized education. Existing KT methods often rely on students' learning interactions to trace their knowledge states by predicting future performance on the given questions. While deep learning-based KT models have achieved improved predictive performance compared with traditional KT models, they often lack interpretability into the captured knowledge states. Furthermore, previous works generally neglect the multiple semantic information contained in knowledge states and sparse learning interactions. In this paper, we propose a novel model named CAKT that couples contrastive learning with attention networks for interpretable knowledge tracing. Specifically, we use three attention-based encoders to model three dynamic factors of the Item Response Theory (IRT) model, based on designed learning sequences. Then, we identify two key properties related to the knowledge states and learning interactions: c

Item response theoryAttention networksContrastive learningKnowledge tracing