Speedy Implementation of Instance-based Learning (IBL) agents in Python

Related tags

Deep LearningSpeedyIBL
Overview

A Python library to create single or multi Instance-based Learning (IBL) agents that are built based on Instance Based Learning Theory (IBLT) 1

References

[1] Cleotilde Gonzalez, Javier F. Lerch and Christian Lebiere (2003), Instance-based learning in dynamic decision making, Cognitive Science, 27, 591-635. DOI: 10.1016/S0364-0213(03)00031-4.

[2] Thuy Ngoc Nguyen, Duy Nhat Phan, Cleotilde Gonzalez (2021), SpeedyIBL: A Solution to the Curse of Exponential Growth in Instance-Based Learning Models of Decisions from Experience

Installation of speedy IBL

%pip install -U speedyibl
Requirement already satisfied: speedyibl in /usr/local/lib/python3.7/dist-packages (0.0.9)
Requirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (from speedyibl) (0.37.0)
Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from speedyibl) (0.8.9)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from speedyibl) (1.19.5)
Requirement already satisfied: setuptools>=42 in /usr/local/lib/python3.7/dist-packages (from speedyibl) (57.4.0)

Import libraries

from speedyibl import Agent
import random 
import numpy as np
import matplotlib.pyplot as plt

Illustration of IBL for Binary Choice Task

Task description

In each episode, the agent is required to choose one of two options: Safe or Risky. One option is Safe and it yields a fixed medium outcome (i.e., 3) every time it is chosen. The other option is Risky, and it yields a high outcome (4) with some probability 0.8, and a low outcome (0) with the complementary probability 0.2.

agent = Agent(default_utility=4.4) #define the agent with default_utility = 4.4, noise = 0.25 decay = 0.5
options = ['safe','risky'] # set of options
runs = 100 # number of runs (participants)
episodes = 100 # number of episodes (trials, rounds)
average_pmax = [] # to store average of performance (proportion of maximum reward expectation choice)

for r in range(runs):
  pmax = []
  agent.reset() #clear the memory for a new run
  for e in range(episodes):     

    choice = agent.choose(options)
    if choice == 'safe':
      agent.respond(3)
    elif random.random() <= 0.8:
      agent.respond(4)
    else:
      agent.respond(0)
    pmax.append(choice == 'risky')
  average_pmax.append(pmax)

Plot the result

plt.plot(range(episodes), np.mean(np.asarray(average_pmax),axis=0), 'o-', color='darkgreen', markersize=2, linestyle='--', label='speedyIBL')

plt.xlabel('Episode')
plt.ylabel('PMAX')
plt.title('Binary choice')
plt.grid(True)
plt.show()

png

Illustration of IBL for Insider Attack Game

Task description

In this game, players take the role of the attacker and their goal is to score points by “hacking” computers to steal proprietary data.

TARGETS = [ [ { "payment": 2, "penalty":  -1, "monitored_probability": 0.22 },
              { "payment": 8, "penalty":  -5, "monitored_probability": 0.51 },
              { "payment": 9, "penalty":  -9, "monitored_probability": 0.42 },
              { "payment": 9, "penalty": -10, "monitored_probability": 0.40 },
              { "payment": 2, "penalty":  -6, "monitored_probability": 0.08 },
              { "payment": 5, "penalty":  -5, "monitored_probability": 0.36 } ],
            [ { "payment": 5, "penalty":  -3, "monitored_probability": 0.41 },
              { "payment": 8, "penalty":  -5, "monitored_probability": 0.48 },
              { "payment": 7, "penalty":  -6, "monitored_probability": 0.41 },
              { "payment": 8, "penalty":  -9, "monitored_probability": 0.37 },
              { "payment": 5, "penalty":  -7, "monitored_probability": 0.27 },
              { "payment": 2, "penalty":  -4, "monitored_probability": 0.05 } ],
            [ { "payment": 3, "penalty":  -3, "monitored_probability": 0.30 },
              { "payment": 9, "penalty":  -4, "monitored_probability": 0.60 },
              { "payment": 6, "penalty":  -6, "monitored_probability": 0.40 },
              { "payment": 5, "penalty":  -8, "monitored_probability": 0.29 },
              { "payment": 3, "penalty":  -6, "monitored_probability": 0.20 },
              { "payment": 2, "penalty":  -2, "monitored_probability": 0.20 } ],
            [ { "payment": 4, "penalty":  -3, "monitored_probability": 0.37 },
              { "payment": 6, "penalty":  -3, "monitored_probability": 0.51 },
              { "payment": 7, "penalty":  -7, "monitored_probability": 0.40 },
              { "payment": 5, "penalty": -10, "monitored_probability": 0.24 },
              { "payment": 5, "penalty":  -9, "monitored_probability": 0.26 },
              { "payment": 3, "penalty":  -4, "monitored_probability": 0.23 } ] ]

COVERAGE = [ [ { 2, 6 }, { 2, 4 }, { 2, 5 }, { 2, 4 }, { 1, 3 },
               { 2, 4 }, { 1, 3 }, { 1, 3 }, { 2, 4 }, { 2, 6 },
               { 2, 6 }, { 2, 4 }, { 1, 3 }, { 2, 4 }, { 2, 4 },
               { 1, 3 }, { 3, 6 }, { 2, 4 }, { 2, 4 }, { 3, 6 },
               { 1, 3 }, { 2, 4 }, { 3, 6 }, { 2, 4 }, { 1, 3 } ],
             [ { 2, 5 }, { 1, 3 }, { 1, 3 }, { 3, 6 }, { 1, 3 },
               { 2, 4 }, { 1, 3 }, { 2, 4 }, { 1, 3 }, { 1, 4 },
               { 1, 3 }, { 1, 3 }, { 2, 5 }, { 1, 3 }, { 1, 3 },
               { 1, 3 }, { 2, 5 }, { 2, 4 }, { 2, 4 }, { 1, 3 },
               { 1, 3 }, { 2, 4 }, { 2, 4 }, { 3, 6 }, { 2, 5 } ],
             [ { 2, 5 }, { 3, 6 }, { 2, 4 }, { 2, 5 }, { 2, 5 },
               { 2, 6 }, { 2, 6 }, { 1, 3 }, { 2, 4 }, { 1, 3 },
               { 2, 4 }, { 1, 3 }, { 1, 3 }, { 2, 6 }, { 2, 5 },
               { 1, 3 }, { 2, 4 }, { 1, 3 }, { 2, 4 }, { 2, 5 },
               { 2, 4 }, { 2, 4 }, { 2, 6 }, { 1, 3 }, { 2, 4 } ],
             [ { 2, 5 }, { 1, 4 }, { 3, 6 }, { 2, 6 }, { 1, 3 },
               { 1, 4 }, { 1, 3 }, { 2, 5 }, { 2, 6 }, { 1, 3 },
               { 1, 3 }, { 3, 6 }, { 2, 4 }, { 1, 4 }, { 1, 4 },
               { 1, 3 }, { 1, 3 }, { 1, 4 }, { 1, 3 }, { 2, 5 },
               { 3, 6 }, { 1, 3 }, { 1, 3 }, { 3, 6 }, { 1, 4 } ] ]

TRAINING_COVERAGE = [ { 2, 5 }, { 2, 4 }, { 1 , 3 }, { 1, 3 }, { 1, 3 } ]

SIGNALS = [ [ { 3, 4 }, { 3, 6 }, { 3, 6 }, { 3, 5, 6 }, { 2, 6 },
              { 3, 6 }, { 2, 4}, { 2, 6 }, { 3, 6 }, { 1, 3, 4 },
              { 3, 4 }, { 1, 3 }, { 4, 6 }, { 5}, { 3, 6 },
              { 2, 4 }, { 5 }, { 3 }, { 6 }, { 2, 4 },
              { 2, 4 }, set(), {2, 4, 5 }, { 3 }, { 5, 6 } ],
            [ { 3, 4 }, { 2, 4 }, { 2, 4, 5 }, { 4, 5 }, { 4, 5 },
              { 1, 3, 6 }, { 2 }, { 3 }, { 5 }, set(),
              { 2, 5 }, { 2, 5 }, {3, 4 }, { 2, 5 }, { 2, 4, 5 },
              { 4, 5 }, { 3, 4 }, { 3, 5, 6 }, { 1, 5}, { 2, 5 },
              { 2 }, { 1, 5 }, { 1, 3, 5 }, { 4 }, { 1, 3, 4, 6 } ],
            [ { 1, 3, 6 }, { 2, 4 }, set(), { 1, 3, 4 }, { 3 },
              { 1, 4, 5 }, { 5 }, { 2, 4}, { 1, 3, 5 }, set(),
              { 1, 3, 5 }, { 2 }, { 2, 4, 5 }, { 5 }, { 3, 4 },
              { 2, 4, 5, 6 }, { 1, 3, 5 }, { 2, 4, 6 }, { 1, 3 }, { 1, 4 },
              { 5 }, {3 }, set(), { 2, 5, 6 }, { 1, 3, 5, 6 } ],
            [ { 6 }, { 3 }, { 2, 4 }, { 4, 5}, { 6 },
              { 3, 5 }, { 4 }, { 3, 4, 6 }, { 1, 3, 4, 5 }, { 2, 4, 6 },
              {4, 5 }, { 2, 5 }, { 1, 5, 6 }, { 2, 3, 6 }, { 2, 3 },
              { 5 }, { 2, 4, 5, 6 }, { 2, 3, 5, 6 }, { 2, 4, 5 }, { 1, 3, 4, 6 },
              { 2, 4, 5 }, { 4, 5 }, { 4 }, { 4, 5 }, { 3, 5, 6 } ] ]

TRAINING_SIGNALS = [ { 3, 4 }, {1, 3, 6 }, { 5 }, { 2, 5 }, {2, 4, 5} ]

for clist, slist in zip(COVERAGE, SIGNALS):
    for c, s in zip(clist, slist):
        s.update(c)

TARGET_COUNT = len(TARGETS[0])
BLOCKS = len(TARGETS)
TRIALS = len(COVERAGE[0])

selection_agent = Agent(default_utility=None,mismatchPenalty = 2.5)
attack_agent = Agent(default_utility=None)
selection_agent = Agent(mismatchPenalty = 2.5) #define the agents with default parameters
selection_agent.similarity([0,1], lambda x, y: 1 - abs(x - y) / 10)
selection_agent.similarity([2], lambda x, y: 1 - abs(x -y))

attacks = [0] * BLOCKS * TRIALS

runs = 1000 # number of runs (participants)
data = []

for p in range(runs):
  total = 0
  selection_agent.reset()
  selection_agent.similarity([0,1], lambda x, y: 1 - abs(x - y) / 10)
  selection_agent.similarity([2], lambda x, y: 1 - abs(x -y))
  
  attack_agent.reset()
  dup = random.randrange(5)
  for i in range(5):
      n = random.randrange(TARGET_COUNT)
      x = TARGETS[1][n]
      covered = n + 1 in TRAINING_COVERAGE[i]
      selection_agent.prepopulate((i + 1,
                                                (x["payment"],
                                                x["penalty"],
                                                x["monitored_probability"])),
                                                x["penalty" if covered else "payment"])
      attack_agent.prepopulate((True, n + 1 in TRAINING_SIGNALS[i]),x["penalty" if covered else "payment"])
      if i == dup:
          # x = TARGETS[1][5]
          selection_agent.prepopulate((6,
                                      (x["payment"],
                                      x["penalty"],
                                      x["monitored_probability"])),
                                      x["penalty" if covered else "payment"])
  attack_agent.prepopulate((False,False),0)
  attack_agent.prepopulate((False,True),0)
  attack_agent.prepopulate((True,False),10)
  attack_agent.prepopulate((False,True),5)
  
  for b in range(BLOCKS):
      sds = [ (i + 1,
                                        (x["payment"],
                                        x["penalty"],
                                        x["monitored_probability"]))
                        for x, i in zip(TARGETS[b], range(TARGET_COUNT)) ]

      for t in range(TRIALS):
          selected = selection_agent.choose(sds)[0]
          warned = selected in SIGNALS[b][t]
          pmnt = TARGETS[b][selected - 1]["payment"]
          attack = attack_agent.choose([(True, warned),
                                        (False, warned)])[0]
          covered = selected in COVERAGE[b][t]
          if not attack:
              payoff = 0
          else:
              payoff = TARGETS[b][selected - 1]["penalty" if covered else "payment"]
              attacks[b * 25 + t] += 1
          total += payoff
          attack_agent.respond(payoff)
          selection_agent.respond(payoff)
          data.append([p+1, b+1,t+1,b*25+t+1, selected, int(warned), int(covered),int(attack),payoff, total])

Plot the result

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(data)

plt.plot(range(1,101), df.groupby(3).mean()[8], 'o-', color='darkgreen', markersize=2, linewidth =2, linestyle='--',label='SpeedyIBL')
plt.xlabel('Round')
plt.ylabel('Average Reward')
plt.grid(True)
plt.legend()
<matplotlib.legend.Legend at 0x7efc84231690>

png

Build an IBL Agent with an Equal Delay Feedback Mechanism

This model will be employed to perform the tasks following

from speedyibl import Agent
from collections import deque
class AgentIBL(Agent):

	# """ Agent """
	def __init__(self, outputs, default_utility = 0.1, Hash = True, delay_feedback = True):
		super(AgentIBL, self).__init__(default_utility=default_utility)
		# '''
		# :param dict config: Dictionary containing hyperparameters
		# '''
		self.outputs = outputs
		self.options = {}
		self.episode_history = []
		self.hash = Hash
		self.delay_feedback = delay_feedback

	def generate_options(self,s_hash):
		self.options[s_hash] = [(s_hash, a) for a in range(self.outputs)]
	
	def move(self, o, explore=True):
		# '''
		# Returns an action from the IBL agent instance.
		# :param tensor: State/Observation
		# '''
		if self.hash:
			s_hash = hash(o.tobytes())
		else:
			s_hash = o
		if s_hash not in self.options:
			self.generate_options(s_hash)
		options = self.options[s_hash]
		choice = self.choose(options)
		self.last_action = choice[1]

		self.current = s_hash

		return self.last_action



	def feedback(self, reward):

		self.respond(reward)

		#episode history
		if self.delay_feedback and (len(self.episode_history) == 0 or self.current != self.episode_history[-1][0]):
			self.episode_history.append((self.current,self.last_action,reward,self.t))
			

	def delayfeedback(self, reward):		
		self.equal_delay_feedback(reward, self.episode_history)

Illustration of IBL for Cooperative Navigation

Task description

In this task, three agents must cooperate through physical actions to reach a set of three landmarks (3 green landmarks). The agents can observe the relative positions of other agents and landmarks, and are collectively rewarded based on the number of the landmarks that they cover. For instance, if all the agents cover only one landmark, they receive one point. By contrast, if they all can cover the three landmarks, they got the maximum of three points. Simply put, the agents want to cover all of the landmarks, so they need to learn to coordinate the landmark they must cover.

Install and call the cooperative navigation environment

!pip install -U vitenv
from vitenv import Environment
env = Environment('NAVIGATION_V1')
Collecting vitenv
  Downloading vitenv-0.0.4-py3-none-any.whl (27 kB)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from vitenv) (1.19.5)
Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from vitenv) (4.1.2.30)
Installing collected packages: vitenv
Successfully installed vitenv-0.0.4

Run experiments

runs = 100
episodes = 100
steps = 2500
number_agents = 3

data = []

from copy import deepcopy

for run in range(runs):

  agents = []
  for i in range(number_agents): 
      agents.append(AgentIBL(env.out,default_utility=2.5)) # Init agent instances

  for i in range(episodes):
      
    # Run episode
    observations = env.reset() # Get first observations
    episode_reward = 0

    for j in range(steps):
      if j == steps-1:
          env.env.t_episode = True
      #######################################
      arriveds = deepcopy(env.env.arriveds)
      actions = [4,4,4]
      for a in range(number_agents):
          if not arriveds[a]:
              actions[a] = agents[a].move(observations[a])

      observations, rewards, t = env.step(actions)

      if j == steps-1:
          t = True

      for a, r in zip(range(number_agents),rewards):
          if not arriveds[a]: 
              agents[a].feedback(r)  
      if t:         
          for agent, r in zip(agents, rewards):
            if r > 0:
              agent.delayfeedback(r) 
            agent.episode_history = []


      episode_reward += rewards[0]
      if t: 
        break # If t then terminal state has been reached
    data.append([run, i, j, episode_reward])
  # print('Finished ', run, '-th run')

Plot results

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(data)

plt.plot(range(1,101), df.groupby(1).mean()[3], 'o-', color='darkgreen', markersize=2, linewidth =2, linestyle='--',label='SpeedyIBL')
plt.xlabel('Episode')
plt.ylabel('Average Reward')
plt.title('Cooperative navigation')
plt.grid(True)
plt.legend()
<matplotlib.legend.Legend at 0x7f37d3ec1a50>

png

Illustration of IBL for Minimap

Task description

The task is inspired by a search and rescue scenario, which involves an agent being placed in a building with multiple rooms and tasked with rescuing victims. Victims have been scattered across the building and their injuries have different degrees of severity with some needing more urgent care than others. In particular, there are 34 victims grouped into two categories (24 green victims and 10 yellow victims). There are many obstacles (walls) placed in the path forcing the agent to look for alternate routes. The agent's goal is to rescue as many of these victims as possible. The task is simulated as a $93 \times 50$ grid of cells which represents one floor of this building. Each cell is either empty, an obstacle or a victim. The agent can choose to move left, right, up or down, and only move one cell at a time.

Install and call the MINIMAP environment

!pip install -U vitenv
from vitenv import Environment
env = Environment('MINIMAP_V1')
Requirement already satisfied: vitenv in /usr/local/lib/python3.7/dist-packages (0.0.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from vitenv) (1.19.5)
Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from vitenv) (4.1.2.30)

Run experiments

runs = 5
episodes = 100
steps = 2500
number_agents = 3

data = []

for run in range(runs):

  
  agent = AgentIBL(env.out,default_utility=0.1) # Init agent instances

  for i in range(episodes):
      
    # Run episode
    observation = env.reset() # Get first observations
    episode_reward = 0

    for j in range(steps):
      #######################################
      action = agent.move(observation)

      observation, reward, t = env.step(action)

      if j == steps-1:
          t = True

      agent.feedback(reward)
      if reward > 0:
          agent.delayfeedback(reward)
          episode_reward += reward 
          agent.episode_history = []

      if t: 
        agent.episode_history = [] 
        break # If t then terminal state has been reached
    data.append([run, i, j, episode_reward])
  print('Finished ', run, '-th run')
Finished  0 -th run
Finished  1 -th run
Finished  2 -th run
Finished  3 -th run
Finished  4 -th run

Plot the result

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(data)

plt.plot(range(1,101), df.groupby(1).mean()[3], 'o-', color='darkgreen', markersize=2, linewidth =2, linestyle='--',label='SpeedyIBL')
plt.xlabel('Episode')
plt.ylabel('Average Reward')
plt.title('MINIMAP V1')
plt.grid(True)
plt.legend()
<matplotlib.legend.Legend at 0x7f37d44ded90>

png

Illustration of IBL for Firemen Task

Task description

The task replicates the coordination in firefighting service wherein agents need to pick up matching items for extinguishing fire. The task is simulated in a gridworld of size $11\times 14$. Two agents located within the gridworld are tasked with locating an equipment pickup area and choosing one of the firefight items. Afterwards, they need to navigate and find the location of fire (F) to extinguish it. The task is fully cooperative as both agents are required to extinguish one fire.

Install and call the FIREMAN environment

!pip install -U vitenv
from vitenv import Environment
env = Environment('FIREMEN_V1')
Requirement already satisfied: vitenv in /usr/local/lib/python3.7/dist-packages (0.0.4)
Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from vitenv) (4.1.2.30)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from vitenv) (1.19.5)

Run experiments

runs = 10
episodes = 100
steps = 2500
number_agents = 2

data = []

from copy import deepcopy

for run in range(runs):

  agents = []
  for i in range(number_agents): 
      agents.append(AgentIBL(env.out,default_utility=13)) # Init agent instances

  for i in range(episodes):
      
    # Run episode
    observations = env.reset() # Get first observations
    episode_reward = 0

    for j in range(steps):
      #######################################
      actions = []
      for agent, o in zip(agents,observations):
          actions.append(agent.move(o))
      observations, rewards, t = env.step(actions)

      for agent, r in zip(agents, rewards):
        agent.feedback(r)  
      if t:         
          for agent, r in zip(agents, rewards):
            agent.delayfeedback(r) 
            agent.episode_history = []

      if j == steps-1:
          t = True

      episode_reward += rewards[0]
      if t: 
        for agent in agents:
          agent.episode_history = []
        break # If t then terminal state has been reached
    data.append([run, i, j, episode_reward])
  print('Finished ', run, '-th run')
Finished  0 -th run
Finished  1 -th run
Finished  2 -th run
Finished  3 -th run
Finished  4 -th run
Finished  5 -th run
Finished  6 -th run
Finished  7 -th run
Finished  8 -th run
Finished  9 -th run

Plot the result

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(data)

plt.plot(range(1,101), df.groupby(1).mean()[3], 'o-', color='darkgreen', markersize=2, linewidth =2, linestyle='--',label='SpeedyIBL')
plt.xlabel('Episode')
plt.ylabel('Average Reward')
plt.title('FIREMEN TASK')
plt.grid(True)
plt.legend()
<matplotlib.legend.Legend at 0x7f53fd40df10>

png

Illustration of IBL for Tasks from GymAI

Install and call the CartPole Task

%pip install gym
import gym 
env = gym.make('CartPole-v1')
Requirement already satisfied: gym in /usr/local/lib/python3.7/dist-packages (0.17.3)
Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from gym) (1.5.0)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from gym) (1.4.1)
Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym) (1.3.0)
Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from gym) (1.19.5)
Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym) (0.16.0)

Run experiments

runs = 100
episodes = 100
steps = 2500
number_agents = 1

data = []

for run in range(runs):

  
  agent = AgentIBL(env.action_space.n,default_utility=11) # Init agent instances

  for i in range(episodes):
      
    # Run episode
    observation = env.reset() # Get first observations
    episode_reward = 0

    for j in range(steps):
      #######################################
      action = agent.move(observation)

      observation, reward, t, info = env.step(action)

      if j == steps-1:
          t = True

      agent.feedback(reward)
      if reward > 0:
          agent.delayfeedback(reward)
          episode_reward += reward 
          agent.episode_history = []

      if t: 
        agent.episode_history = [] 
        break # If t then terminal state has been reached
    data.append([run, i, j, episode_reward])
  # print('Finished ', run, '-th run')

Plot the result

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame(data)

plt.plot(range(1,episodes+1), df.groupby(1).mean()[3], 'o-', color='darkgreen', markersize=2, linewidth =2, linestyle='--',label='SpeedyIBL')
plt.xlabel('Episode')
plt.ylabel('Average Reward')
plt.title('CartPole Game')
plt.grid(True)
plt.legend()
<matplotlib.legend.Legend at 0x7f810f1ee590>

png

Owner
GitHub Repository
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

scikit-learn 52.5k Jan 08, 2023
Mscp jamf - Build compliance in jamf

mscp_jamf Build compliance in Jamf. This will build the following xml pieces to

Bob Gendler 3 Jul 25, 2022
Code for HodgeNet: Learning Spectral Geometry on Triangle Meshes, in SIGGRAPH 2021.

HodgeNet | Webpage | Paper | Video HodgeNet: Learning Spectral Geometry on Triangle Meshes Dmitriy Smirnov, Justin Solomon SIGGRAPH 2021 Set-up To ins

Dima Smirnov 61 Nov 27, 2022
Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models

Clara Meister 50 Nov 12, 2022
Efficient Training of Audio Transformers with Patchout

PaSST: Efficient Training of Audio Transformers with Patchout This is the implementation for Efficient Training of Audio Transformers with Patchout Pa

165 Dec 26, 2022
OpenVisionAPI server

🚀 Quick start An instance of ova-server is free and publicly available here: https://api.openvisionapi.com Checkout ova-client for a quick demo. Inst

Open Vision API 93 Nov 24, 2022
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Code for CCA-SSG model proposed in the NeurIPS 2021 paper From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.

Hengrui Zhang 44 Nov 27, 2022
Source code of AAAI 2022 paper "Towards End-to-End Image Compression and Analysis with Transformers".

Towards End-to-End Image Compression and Analysis with Transformers Source code of our AAAI 2022 paper "Towards End-to-End Image Compression and Analy

37 Dec 21, 2022
Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

(ACMMM 2021 Oral) SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment This repository shows two tasks: Face landmark detection and Fac

BoomStar 51 Dec 13, 2022
Sentiment analysis translations of the Bhagavad Gita

Sentiment and Semantic Analysis of Bhagavad Gita Translations It is well known that translations of songs and poems not only breaks rhythm and rhyming

Machine learning and Bayesian inference @ UNSW Sydney 3 Aug 01, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
The best solution of the Weather Prediction track in the Yandex Shifts challenge

yandex-shifts-weather The repository contains information about my solution for the Weather Prediction track in the Yandex Shifts challenge https://re

Ivan Yu. Bondarenko 15 Dec 18, 2022
3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A - Continual Learning Classification Challenge

Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay 3rd Place Solution for ICCV 2021 Workshop SS

Rifki Kurniawan 6 Nov 10, 2022
Testability-Aware Low Power Controller Design with Evolutionary Learning, ITC2021

Testability-Aware Low Power Controller Design with Evolutionary Learning This repo contains the source code of Testability-Aware Low Power Controller

Lee Man 1 Dec 26, 2021
🧑‍🔬 verify your TEAL program by experiment and observation

Graviton - Testing TEAL with Dry Runs Tutorial Local Installation The following instructions assume that you have make available in your local environ

Algorand 18 Jan 03, 2023
A large-image collection explorer and fast classification tool

IMAX: Interactive Multi-image Analysis eXplorer This is an interactive tool for visualize and classify multiple images at a time. It written in Python

Matias Carrasco Kind 23 Dec 16, 2022
DI-HPC is an acceleration operator component for general algorithm modules in reinforcement learning algorithms

DI-HPC: Decision Intelligence - High Performance Computation DI-HPC is an acceleration operator component for general algorithm modules in reinforceme

OpenDILab 185 Dec 29, 2022
Based on Stockfish neural network(similar to LcZero)

MarcoEngine Marco Engine - interesnaya neyronnaya shakhmatnaya set', kotoraya ispol'zuyet metod samoobucheniya(dostizheniye khoroshoy igy putem proboy

Marcus Kemaul 4 Mar 12, 2022
Subpopulation detection in high-dimensional single-cell data

PhenoGraph for Python3 PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") repr

Dana Pe'er Lab 42 Sep 05, 2022
Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

1.3k Dec 25, 2022