Search Results for "space"

...The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.

...The AVI format allows recordings to replay at the correct frame rate on media players. If a microphone is installed then a WAV file is also created and stored in the AVI file. The ESP32 cannot support all of the features as it will run out of heap space. For better functionality and performance, use one of the new ESP32S3 camera boards, eg Freenove ESP32S3 Cam, and ESP32S3 XIAO Sense, but avoid no-name boards marked ESPS3 RE:1.0.

The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. ...

...The basic idea of Particle Swarm Optimization is the emulation of the social behaviour of, e.g., a flock of birds, as a stochastic optimisation method. Specifically, a particle is an entity representing a solution in the search space. Several particles cooperate inside an algorithmic flow to occupy positions close to the best solution. Development takes place at https://github.com/zfoxer/PsoPath

...This is needed because KenLM doesn't support a make install step.wav2letter++ expects audio and transcription data to be prepared in a specific format so that they can be read from the pipelines. Each dataset (test/valid/train) needs to be in a separate file with one sample per line. A sample is specified using 4 columns separated by space (or tabs).

As a general data structure with strong expressive ability, graphs can be used to describe many problems in the real world, such as user networks in social scenarios, user and commodity networks in e-commerce scenarios, communication networks in telecom scenarios, and transaction networks in financial scenarios. and drug molecule networks in medical scenarios, etc. Data in the fields of text, speech, and images is easier to process into a grid-like type of Euclidean space, which is suitable for processing by existing deep learning models. Graph is a data type in non-Euclidean space and cannot be directly applied to existing methods, requiring a specially designed graph neural network system. Graph-based learning methods such as graph neural networks combine end-to-end learning with inductive reasoning, and are expected to solve a series of problems such as relational reasoning and interpretability that deep learning cannot handle.

...Instead of focusing on one task, StarSpace supports multi-task and multi-domain setups—for instance, you can train embeddings so that textual queries match item descriptions, sentences map to labels, or users align with liked items in the same embedding space. The training objective is contrastive: for a given query embedding, positive and negative examples are sampled and the model is optimized to score positive higher than negatives. The library supports a variety of tasks (text classification, nearest-neighbor search, recommendation, entity linking) with simple configuration. It includes efficient batching, negative sampling strategies, and on-the-fly embedding updates.

...To facilitate balanced workload distribution, we have proposed a general framework for symmetric all-pairs computation by building provable bijective functions between job identifier and coordinate space for the first time.

The Star Ships Learning Framework (SSLF) provides basic routines and methods in order to interact with the space shooter Star Ships V2.1. It provides the necessary interfaces for controlling the program's ships and its logic from outside the game.

The ClusterMX program implements various clustering algorithms including 1) K-Means clustering optimized by random walks; 2) Weighted K-Means (applying force filed to the multidimensional clustering space); 3) EM Clustering Algorithm; 4) Multi-Model Mean Shift Clustering with Random Sampling; 5) Unsupervised K-Wishart clustering.

ISS: a toolset for visual artists, composers and researchers in the area of: artificial life, sound synthesis, interactive art installations, immersive user interaction, sound spacialization. More information can be found on http://swarms.cc

SMILE = Speech & Music Interpretation by Large Space Extraction openSMILE is a fast, real-time (audio) feature extraction utility for automatic speech, music and paralinguistic recognition research developed originally at TUM in the scope of the EU-project SEMAINE, now maintained and supported by audEERING.

A software toolkit that implements the CoSy Architecture Schema for developing integrated intelligent robotic systems. The toolkit focuses on supporting on a space of information-processing architectures based on shared working memories.

A C++ implementation of Dynamic Field Theory (DFT), Amari kind of space-time continuous fields representing activation of populations in neural cortex. Source code for the GUI is available through SVN.

The Crystallizing Substochastic Sequential Machine Extractor, CrySSMEx, is an algorithm for extracting Finite State Machines from Recurrent Neural Networks. Input: sequential data generated from RNNs. Output: (stochastic) FSMs and state space quantizers.

A framework for bridging the gap between planning, execution, and control, especially with Stanford's whole-body operational space control framework.

This project is a space-based real-time strategy game.

Blackboard implements a lightweight, portable tuple space suitable for multi-agent system and distributed component design. Supports implicit invocation via content-filtered asynchronous events, blocking call semantics, and shared memory messaging.

SenseRank Sys: - builds the dictionaries (multidim matrices) of words’ values; - for the set utterance in certain language builds a figure in multidimensional space (in the matrix space) of values (visual schema), which is topological view of sense

AGISim is a framework for the creation of virtual worlds for artificial intelligence research, allowing AI and human controlled agents to interact in realtime within sensory-rich contexts. AGISim is built on the Crystal Space 3D game engine.

A landscape viewable outside of a window invites users to participate with the outside environment by actively changing it. A familiar space becomes disorienting when interaction with a familiar setup responds contradictorily to users expectations. This

a C++ template container implementation of k-dimensional space sorting based on the kd-tree data structure. THIS PROJECT HAS MOVED TO ALIOTH.DEBIAN.ORG

Program to detect human faces on a digital picture. It uses a MLP neural net that receive relative distances from face distribution clusters of the image space. Written in C++. Requires newmat and libann libraries.

The PSU Mars Rover Software System is a collection of modules connected via shared memory space which allow the operation of various sub-systems to control the rover in all of its tasks; especially navigation.

3D space trade / action / colonization game seen from eyes of your player/cockpit . longterm : complex features in all aspects of game, multiplayer. Generally every can join , more good people = better.