Automatic variational inference with latent categorical variablesTomasz Kusmierczyk
Advances in gradient-based inference have made distributional approximations for posterior distribution of latent-variable models easy, but only for continuous latent spaces. Models with discrete latent variables still require analytic marginalization, continuous relaxations, or specialized algorithms that are difficult to generalize already for minor variations of the model. Discrete normalizing flows could, in principle, be used as approximations while allowing efficient gradient-based learning, but are not sufficiently expressive for representing realistic posterior distributions even for simple cases. We overcome this limitation by considering mixtures of discrete normalizing flows instead.
Automatic variational inference with latent categorical variablesTomasz Kusmierczyk
Advances in gradient-based inference have made distributional approximations for posterior distribution of latent-variable models easy, but only for continuous latent spaces. Models with discrete latent variables still require analytic marginalization, continuous relaxations, or specialized algorithms that are difficult to generalize already for minor variations of the model. Discrete normalizing flows could, in principle, be used as approximations while allowing efficient gradient-based learning, but are not sufficiently expressive for representing realistic posterior distributions even for simple cases. We overcome this limitation by considering mixtures of discrete normalizing flows instead.
Color palette is create in visual basic 6.0 for create hexadecimal value of color without using a hex function .
user can make a combination of (RGB) colors.
Color palette is create in visual basic 6.0 for create hexadecimal value of color without using a hex function .
user can make a combination of (RGB) colors.
Talk on Resource Allocation Strategies for Layered Multimedia Multicast ServicesAndrea Tassi
The explosive growth of content-on-the-move, such as video streaming to mobile devices, has propelled research on multimedia broadcast and multicast schemes. Multi-rate transmission strategies have been proposed as a means of delivering layered services to users experiencing different downlink channel conditions. In this presentation, we consider random linear network coding for its inherent reliability features and study two encoding approaches, which are appropriate for layered services. We derive packet error probability expressions and use them as performance metrics in the formulation of resource allocation frameworks. The aim of these frameworks is both the optimization of the transmission scheme and the minimization of the number of broadcast packets on each downlink channel, while offering service guarantees to a predetermined fraction of users. Our proposed frameworks are adapted to the LTE stack and the integrated eMBMS technology. We focus on the delivery of a video service based on the H.264/SVC standard and demonstrate the advantages of layered network coding over multi-rate transmission. Furthermore, we establish that the choice of both the network coding technique and the resource allocation method play a critical role in the footprint of a service, as determined by the quality of each received video layer.
A Fast Near Optimal Vertex Cover Algorithm (NOVCA)Waqas Tariq
This paper describes an extremely fast polynomial time algorithm, the Near Optimal Vertex Cover Algorithm (NOVCA) that produces an optimal or near optimal vertex cover for any known undirected graph G (V, E). NOVCA is based on the idea of (i) including the vertex having maximum degree in the vertex cover and (ii) rendering the degree of a vertex to zero by including all its adjacent vertices. The two versions of algorithm, NOVCA-I and NOVCA-II, have been developed. The results identifying bounds on the size of the minimum vertex cover as well as polynomial complexity of algorithm are given with experimental verification. Future research efforts will be directed at tuning the algorithm and providing proof for better approximation ratio with NOVCA compared to any other available vertex cover algorithms.
This presentation begins with explaining the basic algorithms of machine learning and using the same concepts, discusses in detail 2 supervised learning/deep learning algorithms - Artificial neural nets and Convolutional Neural Nets. The relationship between Artificial neural nets and basic machine learning algorithms such as logistic regression and soft max is also explored. For hands on the implementation of ANN's and CNN's on MNIST dataset is also explained.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2. Algorithm: Binomial_coefficient(n , k)
// purpose: to compute binomial by dynamic programming
//input: non negative integers such as n ≥ k ≥ 0
//output: value of nCk also designated as C(n,k)
for i = 0 to n do
for j = 0 to min(I,k) do
if(j=0 or i = j)
c[i,j] = 1
else
c[i,j] = c[i-1 , j-1] + c[i-1 , j]
end if
end for
end for
Return c[n,k]
3. Warshall’s algorithm
Algorithm: warshall(n, A, p)
// purpose: to compute transitive closure(path matrix)
//input: adjacency matrix A of size n x n
//output: transitive closure(path matrix) of size n x n
Step1: // make a copy of adjacency matrix
for i=0 to n-1 do
for j = 0 to n-1 do
p[i,j] = A[i,j]
end for
end for
4. Step2: // find the transitive closure(path matrix)
for k =0 to n-1 do
for i=0 to n-1 do
for j=0 to n-1 do
if(p[i,j]=0 and (if(p[i,k] =1 and p[k,j] =1)) then
p[i,j]=1
end if
end for
end for
end for
step 3: return
5. Floyds algorithm
Algorithm: Floyd(n, cost , D)
// purpose: to implement Floyd's algorithm for all pairs
shortest path.
//input: cost adjacency matrix cost of size n x n.
//output: shortest distance matrix of size n x n.
// make a copy of cost adjacency matrix
for i=0 to n-1 do
for j = 0 to n-1 do
D[i,j] = cost[i,j]
end for
end for
6. // find the all pairs shortest path
for k =0 to n-1 do
for i=0 to n-1 do
for j=0 to n-1 do
D[i,j]= min( D[i,j], D[i,k] + D[k,j] )
end for
end for
end for
return
7. Knapsack algorithm
Algorithm: KNAPSACK (n, m, w, p, v)
// purpose: to find the optimal solution for the knapsack
problem using dynamic programming.
//input: n - Number of objects to be selected.
//
m - capacity of the knapsack.
//
w – weights of all the objects.
//
p – profits of all the objects.
//output: v - the optimal solution for the number of
objects selected with specified remaining capacity.
8. for i =0 to n do
for j = 0 to m do
if( i = 0 or j = 0 )
v[i , j] = 0
else if (w[i] > j )
v[i , j] = v[ i -1 , j]
else
v[i , j] = max( v[i-1 , j] , v[i-1 , j-w[i]] + p[i])
end if
end for
end for
return