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04 Jun

How In-Context Learning Emerges from Training on Unstructured Data: On the Role of Co-Occurrence, Positional Information, and Noise Structures

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[Submitted on 31 May 2024] View a PDF of the paper titled How In-Context Learning Emerges from Training on Unstructured Data: On the Role of Co-Occurrence, Positional Information, and Noise Structures, by Kevin Christian Wibisono and 1 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) like transformers have impressive in-context learning (ICL) capabilities; they can generate predictions for new queries based on input-output sequences in prompts without parameter updates. While many theories have attempted to explain ICL, they often focus on structured training data similar to ICL tasks, such as regression. In practice, however, these models are trained…

04 Jun

Artemis: Towards Referential Understanding in Complex Videos

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arXiv:2406.00258v1 Announce Type: new Abstract: Videos carry rich visual information including object description, action, interaction, etc., but the existing multimodal large language models (MLLMs) fell short in referential understanding scenarios such as video-based referring. In this paper, we present Artemis, an MLLM that pushes video-based referential understanding to a finer level. Given a video, Artemis receives a natural-language question with a bounding box in any video frame and describes the referred target in the entire video. The key to achieving this goal lies in extracting compact, target-specific video features, where we set a solid baseline by tracking and selecting spatiotemporal…

04 Jun

Embedding-Aligned Language Models

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arXiv:2406.00024v1 Announce Type: new Abstract: We propose a novel approach for training large language models (LLMs) to adhere to objectives defined within a latent embedding space. Our method leverages reinforcement learning (RL), treating a pre-trained LLM as an environment. Our embedding-aligned guided language (EAGLE) agent is trained to iteratively steer the LLM's generation towards optimal regions of the latent embedding space, w.r.t. some predefined criterion. We demonstrate the effectiveness of the EAGLE agent using the MovieLens 25M dataset to surface content gaps that satisfy latent user demand. We also demonstrate the benefit of using an optimal design of a state-dependent…

04 Jun

Distributed ML for IoT

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IntroductionToday, manufacturers’ field maintenance is often more reactive than proactive, which can lead to costly downtime and repairs. Historically, data warehouses have provided a performant, highly structured lens into historical reporting but have left users wanting for effective predictive solutions. However, the Databricks Data Intelligence Platform allows businesses to implement both historical and predictive analysis on the same copy of their data. Manufacturers can leverage predictive maintenance solutions to identify and address potential issues before they become business critical customer facing problems. Databricks provides end-to-end machine learning solutions including tools for data preparation, model training, and root cause analysis reporting.…

04 Jun

Reward Machines for Deep RL in Noisy and Uncertain Environments

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arXiv:2406.00120v1 Announce Type: new Abstract: Reward Machines provide an automata-inspired structure for specifying instructions, safety constraints, and other temporally extended reward-worthy behaviour. By exposing complex reward function structure, they enable counterfactual learning updates that have resulted in impressive sample efficiency gains. While Reward Machines have been employed in both tabular and deep RL settings, they have typically relied on a ground-truth interpretation of the domain-specific vocabulary that form the building blocks of the reward function. Such ground-truth interpretations can be elusive in many real-world settings, due in part to partial observability or noisy sensing. In this paper, we explore the…

04 Jun

A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing

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arXiv:2406.00239v1 Announce Type: new Abstract: Research in neural models inspired by mammal's visual cortex has led to many spiking neural networks such as pulse-coupled neural networks (PCNNs). These models are oscillating, spatio-temporal models stimulated with images to produce several time-based responses. This paper reviews PCNN's state of the art, covering its mathematical formulation, variants, and other simplifications found in the literature. We present several applications in which PCNN architectures have successfully addressed some fundamental image processing and computer vision challenges, including image segmentation, edge detection, medical imaging, image fusion, image compression, object recognition, and remote sensing. Results achieved in these…

04 Jun

LocMoE+: Enhanced Router with Token Feature Awareness for Efficient LLM Pre-Training

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04 Jun

Databricks Nabs Iceberg-Maker Tabular to Spawn Table Uniformity

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(Maksim-Kabakou/Shutterstock) Databricks today announced the acquisition of Tabular, the commercial outfit behind the Apache Iceberg table format, which competes with Databricks’ own Delta format, paving the way for Databricks customers to enjoy more uniformity and less incompatibilities in their data lakehouse environments. The deal reportedly was valued at more than $1 billion. Open table formats have become the new battleground for control of data lakehouses, those data platforms that blend the scalability and flexibility of data lakes with the ACID transactionality and reliability of traditional data warehouses. Apache Hudi, Apache Iceberg, and Databricks’ Delta have been locked in a three-way…

04 Jun

The Next Generation of Databricks Notebooks: Simple and Powerful

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Over the last year, we’ve been listening to feedback and iterating on new ideas with a single goal: to build the best data-focused authoring experience for data scientists, engineers, and SQL analysts. Today, we are excited to introduce the next generation of Databricks Notebooks with a sleek, modern interface and powerful new features designed to simplify coding and data analysis. Key enhancements include:Modern UX: Enjoy a streamlined coding experience with the GA of the new Notebook UI and other features to enhance Notebook organization.New Results Table: Perform no-code data exploration with search and filtering directly on result output. More powerful Python features:…

04 Jun

ADEP: A Novel Approach Based on Discriminator-Enhanced Encoder-Decoder Architecture for Accurate Prediction of Adverse Effects in Polypharmacy

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arXiv:2406.00118v1 Announce Type: new Abstract: Motivation: Unanticipated drug-drug interactions (DDIs) pose significant risks in polypharmacy, emphasizing the need for predictive methods. Recent advancements in computational techniques aim to address this challenge. Methods: We introduce ADEP, a novel approach integrating a discriminator and an encoder-decoder model to address data sparsity and enhance feature extraction. ADEP employs a three-part model, including multiple classification methods, to predict adverse effects in polypharmacy. Results: Evaluation on benchmark datasets shows ADEP outperforms well-known methods such as GGI-DDI, SSF-DDI, LSFC, DPSP, GNN-DDI, MSTE, MDF-SA-DDI, NNPS, DDIMDL, Random Forest, K-Nearest-Neighbor, Logistic Regression, and Decision Tree. Key metrics include…