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Continuous Autoregressive Language Models Compresses a fixed number of tokens into a vector using an autoencoder, and directly models prediction of next vector.

The crypto world shifts in an instant, but @Assemble_io keeps you one step ahead. It interprets the market as a narrative, linking events, sentiment, and price action to reveal where real attention and opportunity is moving.

My 3 model prediction for NJ. the Range Model is based on a 25-75 percentile band. The GOAT Model is based on the quailty of the campaign. The Aggregate Model adds the most divergent of the range to the GOAT and gets the average

ARIMA (Autoregressive Integrated Moving Average) is a powerful statistical model for time series forecasting. It works by capturing non-seasonal trends. Its "AR" (Autoregressive) part uses past values to predict the future, "MA" (Moving Average) uses past forecast errors, and "I"…

There's too many AI models. Usually, you know what you want. But you don't know which model gets you there. @AlloraNetwork changes this. Tell it your goal & it handles the rest. It routes your question to multiple models & gives you the most accurate answer. AI Model…

Hi #EconTwitter! Interested in #MachineLearning methods for macroeconomic 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴? Check out this brand new #econometrics paper where @gibaboaretto (@pucriooficial) & @mcunhamedeiros (@UofIllinois) focus on aggregating disaggregated forecasts. Cool stuff!

Hey Laravel Developers 👩‍💻, 🚀 In my recent tweet, we discovered cool methods like loadSum, loadMin, loadMax, and loadAvg available on a model to get aggregate values from a relation. But guess what? Laravel makes it even simpler! You can eagerly load these aggregates while…

Graphical models are a powerful fusion of graph theory and probability, representing complex relationships between variables as a network of nodes and edges. They are vital in ML and statistics for applications like Bayesian networks, causal inference, and image segmentation.…

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a…

In the ARIMA methodology, the AR part stands for Auto-Regressive model. An AR model suggests that the current value of a time series is a linear combination of its previous values and a random error term. Let's find out more about it! 👇 🧵

Just published a long-awaited #QGIS tutorial showing how to use the powerful Aggregate (Dissolve-with-Statistics) algorithm in #QGIS for calculating area-weighted means. Open hydrological datasets and open-source tools for computing areal precipitation qgistutorials.com/en/docs/3/area…

Generalized Linear Models (GLMs) are a powerful extension of linear regression. They allow for response variables that aren't normally distributed (e.g., binary or count data) by using a "link function." In machine learning, logistic regression (a GLM) is a workhorse for…

New paper "Graph Attention Retrospective". One of the most popular type of models is graph attention networks. These models were introduced to allow a node to aggregate information from the features of neighbor nodes in a non-uniform way arxiv.org/abs/2202.13060

ARIMA models are essential in Time Series forecasting. You can add multiple components to make them fit your particular data: go from a basic AR model to a complex SARIMAX model! 🧵 👇

Everyone says “ensemble beats single models,” but @AlloraNetwork goes further: it forecasts which models will win right now and weights the mix on the fly. That’s the edge context-aware forecasting turns a noisy crowd into a focused signal. ❯ Workers don’t just post…

Mixed models combine fixed effects (consistent across data) and random effects (vary across data) to analyze complex data structures, such as repeated measures or hierarchical data. When used correctly, they can provide more accurate and reliable insights. ✔️ Handles Complex…

The aggregate market knows full integration into power and infrastructure will be more lucrative than asset-light model.

Train an LM made of independent expert LMs (no syncs! no shared params!) ➡️ ➕ new or ➖ existing experts. At. Any. Time. ➡️ Ensemble OR parameter average(!!) to outperform dense & sparse LMs & ensemble baselines with less compute, a fraction of the simultaneous GPU usage. 🌳/n

AI at scale is costly & complex. The fix? Model bundling. 🦾 Our platform combines multiple models, hot-swaps them in milliseconds, & optimizes compute. Read more:

Scale kills yield. When too much capital floods a yield source, returns collapse. That’s why most yield aggregators aren't as appealing when they grow. But Aqua’s 𝗧𝗶𝗱𝗲 & 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗠𝗼𝗱𝗲𝗹 turns scale into strength. Here's how it works. 🧵